Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives, a review

Vol.:(0123456789)
The Jou nal o Supe compu ing (2025) 81:1123
h ps://doi.o g/10.1007/s11227-025-07563-6
Biclus e ing inbioin o ma ics using big da a andHigh
Pe o mance Compu ing applica ions: challenges
andpe spec i es, a e iew
Au elioLópez‑Fe nández4· F anciscoA.Gomez‑Vela1·
DomingoS.Rod iguez‑Baena1· Fe nandoM.Delgado‑Cha es2·
Jo geGonzalez‑Dominguez3
Accep ed: 6 June 2025
© The Au ho (s) 2025
Abs ac
Biclus e ing is a powe ul machine lea ning echnique ha simul aneously g oups
ows and columns in ma ix-based da ase s. Applied o gene exp ession da a in bio-
in o ma ics, i s use has expanded alongside he apid g ow h o high- h oughpu
sequencing echnologies, leading o massi e and complex biological da ase s. This
e iew aims o examine how biclus e ing me hods and hei alida ion s a egies a e
e ol ing o mee he demands o High Pe o mance Compu ing (HPC) and Big Da a
en i onmen s. We p esen a s uc u ed classi ica ion o exis ing app oaches based
on he compu a ional pa adigms hey employ, including MPI/OpenMP, Apache
Hadoop/Spa k, and GPU/CUDA. By syn hesising hese de elopmen s, we highligh
cu en ends and ou line key esea ch challenges. The knowledge ga he ed in his
wo k may suppo esea che s in adap ing and scaling biclus e ing algo i hms o
analyse la ge-scale biomedical da a mo e e icien ly. Ou con ibu ion is in ended o
b idge he gap be ween algo i hmic inno a ion and compu a ional scalabili y in he
con ex o bioin o ma ics and da a-in ensi e applica ions.
Keywo ds Biclus e ing· Big da a· High Pe o mance Compu ing· Bioin o ma ics
Abb e ia ions
ACV A e age co ela ion alue
AWS Amazon web se ices
CC Cheng–Chu ch
CCS The condi ion-dependen co ela ion subg oups
CPU Cen al p ocessing uni
CUDA Compu e uni ied de ice a chi ec u e
EBI Eu opean bioin o ma ics ins i u e
EBIC E olu iona y-based bIClus e ing
ENCODE Encyclopedia o DNA elemen s
Ex ended au ho in o ma ion a ailable on he las page o he a icle
A.López-Fe nández e al.
1123 Page 2 o 52
FCA Fo mal concep analysis
FLOC FLexible o e lapped biClus e ing
FPGA Field-p og ammable ga e a ay
GB Gigaby e
GBC Geome ic biclus e ing algo i hm
GMQL GenoMe ic que y language
GPGPU Gene al-pu pose compu ing on g aphics p ocessing uni s
GPU G aphics p ocessing uni
GTEX Geno ype- issue exp ession p ojec
HDFS Hadoop dis ibu ed ile sys em
HPC High Pe o mance Compu ing
KEGG Kyo o encyclopedia o genes and genomes
LCS Longes common sequence
MATLAB MAT ix labo a o y
MCC Ma hews co ela ion coe icien
MPI Message passing in e ace
MR MapReduce
MSR Mean squa e esidue
NGS Nex -gene a ion sequencing
NMF Non-nega i e ma ix ac o isa ion
NMI No malised mu ual in o ma ion
PBD-SPEA2 Pa allel biclus e ing de ec ion
PC Pa allel coo dina e
PCR Polyme ase chain eac ion
PGAS Pa i ioned global add ess space
POSIX Po able ope a ing sys em in e ace
PPI P o ein–p o ein in e ac ion
RAM Random access memo y
RDD Robus dis ibu ed da ase s
RNA Ribonucleic acid
SM NVIDIA S eaming mul i-p ocesso
SMP Sha ed memo y pa allelism
SPEA2 S eng h pa e o on e olu iona y algo i hm2
SPMD Single p og am, mul iple da a
SPUs S eam p ocesso uni s
SPs S eaming p ocesso s
SQL S uc u ed que y language
STRING Sea ch ool o he e ie al o in e ac ing genes/p o ein
TCGA The cance genome a las
UM Uni ied memo y
Biclus e ing inbioin o ma ics using big da a andHigh… Page 3 o 52 1123
1 In oduc ion
Biclus e ing echniques ha e been success ully applied o se e al esea ch a eas,
including ene gy consump ion [1], economics [2], ading o ecas ing [3], ma ke -
ing [4], o ecommenda ion sys ems [5]. In bioin o ma ics, hese echniques a e
o en used o applica ions such as analysing gene exp ession da a, disco e ing and
anno a ing new unc ionali ies o unclassi ied genes, inding exp ession modules,
econs uc ing biological ne wo ks, elucida ing disease mechanisms, and s a i ying
pa ien s [6, 7]. The applica ion o biclus e ing echniques on gene exp ession da a-
se s has wo main ad an ages o e clus e ing: (a) i g oups bo h genes and expe i-
men al condi ions, which is much close o biology since a subse o genes may ha e
common biological beha iou only unde a subse o expe imen al condi ions o
samples [8], and (b) i conside s g oup o e lapping, allowing genes o con ibu e o
mo e han one biological ac i i y. Despi e hese ad an ages, se e al challenges a ise,
mainly due o he exponen ial g ow h o biological da ase s—in bo h olume and
complexi y—gene a ed by nex -gene a ion sequencing (NGS) echnologies [9] and
he c ea ion o la ge-scale genomic conso iums [10].
The comple e anno a ion and quan i ica ion o he exp ession le el o all genes
and hei iso o ms in a speci ic sample [11], along wi h he da a olume ha ep-
esen s gene sequencing in o ma ion om 100 million o 2 billion indi idual
pa ien s in he yea 2025 [10], demons a e some challenges aced in his ield.
Also, i is essen ial o conside ha his da a may o igina e om a ious sou ces,
be s o ed in se e al o ma s, and ypically con ain noise and a e ac s ha equi e
esolu ion [12]. This inc eases he compu a ional complexi y o biclus e ing
me hods used o e alua e and ex ac meaning ul in o ma ion om la ge biomedi-
cal and biological da ase s.
To add ess hese challenges, High Pe o mance Compu ing (HPC) app oaches,
such as Gene al-Pu pose Compu ing on G aphics P ocessing Uni s (GPGPU),
Big Da a amewo ks, and adi ional pa allel and dis ibu ed compu ing pa a-
digms, ha e been adop ed o imp o e pe o mance and scalabili y [13]. Despi e
ha ing p o en highly use ul in mul iple con ex s, con en ional biclus e ing ech-
niques exhibi a numbe o echnical and compu a ional limi a ions when dealing
wi h ex emely la ge da ase s. These limi a ions can be summa ised as ollows:
• High compu a ional complexi y: Con en ional echniques o en aim o ind
op imal solu ions a he cos o high compu a ional complexi y. This becomes
unsus ainable when inpu ma ices con ain millions o ows and columns. Fo
example, he i s ue biclus e ing algo i hm, he Cheng–Chu ch algo i hm
[14], has app oxima ely quad a ic complexi y in he numbe o ows and col-
umns, making i poo ly scalable.
• Memo y limi a ions: Many algo i hms s o e en i e ma ices o mul iple auxil-
ia y s uc u es in memo y, which becomes un easible wi h e y la ge da ase s.
Fo ins ance, algo i hms like SAMBA [15] may encoun e memo y manage-
men issues when handling gene exp ession da ase s wi h mo e han 100,000
genes and condi ions.
A.López-Fe nández e al.
1123 Page 4 o 52
• Sensi i i y o noise and edundancy: In la ge da ase s, noise is ine i able
and can lead o many spu ious pa e ns. Classical echniques a e no always
designed o e ec i ely il e ou his noise. Algo i hms such as Bimax [16]
sea ch o exac biclus e s (i.e. noise- ee), which makes hem less use ul o
la ge-scale eal-wo ld da a.
In addi ion o hese h ee main issues, he e is also a lack o adap abili y o dis-
ibu ed s uc u es and an inabili y o handle he e ogeneous o dynamic da a.
The impo ance o s udying biclus e ing unde he Big Da a and HPC pa a-
digms lies in he g owing need o ex ac biologically meaning ul pa e ns om
massi e and complex da ase s. Fo example, in he wo k p esen ed by Liu e al.
[17], i e gene exp ession da ase s we e p ocessed, gene a ing a o al o 8280536
biclus e s. F om a genomic conso ium poin o iew, ano he example can be
ound in The Cance Genome A las (TCGA) [18], which p o ides mul i-omics
da a o housands o umou samples ac oss nume ous cance ypes. The iden-
i ica ion o biclus e s in his con ex can help disco e cance -speci ic gene
exp ession modules, s a i y pa ien s based on molecula p o iles, and suppo he
design o a ge ed he apies. Howe e , he olume and complexi y o hese ypes
o da ase means ha adi ional biclus e ing me hods a e insu icien . So a sys-
ema ic examina ion o new biclus e ing app oaches ha in eg a e obus and e i-
cien compu a ional s a egies wi h biological in e p e abili y is equi ed.
Since he publica ion o he wo k by Sa a C. Madei a e al. [19], many s ud-
ies ha e been p esen ed ecen ly whose objec i e is o p esen a classi ica ion o
compa a i e s udy. Despi e his weal h o in o ma ion, o he bes o ou knowl-
edge, none o hese pape s has ocused hei s udy on how new o e en exis ing
biclus e ing echniques ha e me he challenge o being able o adap o a Big
Da a o HPC ecosys em o p ocess he biological and biomedical da ase s ha a e
cu en ly a ailable. The e is also no ela ed wo k on he abili y o biclus e ing
echniques o alida e he la ge numbe o esul s hey gene a e o how biclus e
alida ion me hods add ess his p oblem. Mo eo e , in o de o cope wi h a Big
Da a ecosys em, hese echniques should also ake in o accoun he a ious chal-
lenges hey mus ace when ying o adap o some speci ic HPC me hods. How-
e e , he e a e also no wo ks ha add ess he challenges in ol ed in his ype o
adap a ion.
This pape p esen s an o e iew o esea che s and p ac i ione s o disco e sui -
able biclus e ing solu ions o la ge-scale bioin o ma ics applica ions while p omo -
ing u u e esea ch ha connec s biological knowledge wi h compu a ional scalabil-
i y. This wo k explo es he e olu ion o biclus e ing echniques in esponse o he
inc easing scale and complexi y o bioin o ma ic da ase s. I examines he inco po-
a ion o High Pe o mance Compu ing (HPC) and Big Da a amewo ks as key ena-
ble s o imp o ing he scalabili y and applicabili y o hese me hods in eal-wo ld
biomedical scena ios. Fu he mo e, i analyses he p incipal limi a ions and ongoing
challenges ha pe sis in his in e disciplina y ield, highligh ing he need o con in-
ued me hodological inno a ion o ensu e bo h compu a ional e iciency and biologi-
cal in e p e abili y. The e o e, we can summa ise he con ibu ions o his wo k as
ollows:
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• A sys ema ic and up- o-da e su ey o biclus e ing echniques ha add ess he
challenges posed by la ge-scale biomedical da a.
• A classi ica ion o hese me hods based on hei compu a ional pa adigms and
p ac ical implemen a ions.
• A c i ical analysis o hei biological applica ions and he epo ed compu a ional
pe o mance based on a comp ehensi e e iew o he exis ing li e a u e.
• A summa y o cu en challenges and u u e di ec ions in his ield.
The s uc u e o he es o he pape is as ollows: Sec ion3 p esen s a lis o he
mos ele an biclus e ing e iews. Sec ion4 g oups he p oblems ha HPC and
Big Da a applica ions used by biclus e ing echniques mus add ess o suppo huge
da ase s and ob ain hei esul s in he sho es possible ime. Biclus e ing echniques
based on Big Da a and HPC applica ions a e examined in Sec ion 5. Sec ion 6
e iews he di e en biclus e alida ion me hods and alida ion p ocesses imple-
men ed in biclus e ing echniques ha can alida e la ge amoun s o esul s in he
sho es possible execu ion ime. Sec ion7 desc ibes he challenges ha biclus e ing
and i s alida ion measu es mus ace o adap o a Big Da a ecosys em. Finally, Sec-
ion8 summa ises he main conclusions de i ed om his wo k.
2 Re iew me hodology
This s udy employs a s uc u ed p ocess based on he PRISMA 2020 p inciples o
gua an ee scien i ic igou , anspa ency, and epea abili y. Two dis inc ye in e con-
nec ed analyses we e conduc ed o encapsula e he ecen ad ancemen s in biclus e -
ing me hodologies employed in bioin o ma ics, pa icula ly in ligh o Big Da a and
high-pe o mance compu ing issues.
The ini ial s udy concen a ed on disco e ing e iew a icles and su eys ha
in es iga e biclus e ing echniques used in bioin o ma ics. The aim was o e alua e
he dep h, b ead h, and emphasis o p e ious e alua ions, speci ically wi h hei han-
dling o scalabili y, alida ion, and p ac ical use ulness in bioin o ma ics.
The second s udy ocused on o iginal esea ch a icles ha p oposed o imple-
men ed biclus e ing me hods inside High Pe o mance Compu ing (HPC) and Big
Da a en i onmen s. This encompasses biclus e ing me hods using adi ional pa al-
lel and dis ibu ed models (e.g. MPI, OpenMP), Big Da a amewo ks (e.g. Apache
Spa k, Hadoop), and GPGPU accele a ion (e.g. CUDA, OpenCL). The objec i e is
o delinea e ends, p oblems, and he echnological de iciencies in scalable biclus e
deploymen s and hei applica ions wi hin he bioin o ma ics domain.
2.1 Sea ch s a egy andda a sou ces
Bo h analyses employed sys ema ic sea ch s a egies ac oss h ee majo da abases:
Scopus, PubMed, and Google Schola . The sea ches we e ca ied ou in May 2025
and we e limi ed o pee - e iewed a icles published be ween 1 Janua y 2009 and 25
May 2025. Only a icles w i en in English we e included.

A.López-Fe nández e al.
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Fo he e iew and su ey wo ks, que ies combined e ms such as: ”biclus e ing”,
”co-clus e ing”, ”su ey”, ” e iew”, ”bioin o ma ics”, and ”gene exp ession”.
Fo he HPC/Big Da a implemen a ions, que ies included combina ions o :
”biclus e ing”, ”co-clus e ing”, ”pa allel compu ing”, ”dis ibu ed compu ing”,
”high-pe o mance compu ing”, ”HPC”, ”Big Da a”, ”Apache Spa k”, ”Hadoop”,
”GPU”, ”GPGPU”, ”CUDA”, and ”bioin o ma ics”.
In all cases, Boolean ope a o s and pla o m-speci ic syn ax we e used. Al hough
all analyses we e pe o med o all pla o ms, some illus a i e que ies o each da a-
base a e shown below.
PubMed que y (su ey and e iew iden i ica ion):
((biclus e ing[Ti le/Abs ac ] OR ”co-
clus e ing”[Ti le/Abs ac ]) AND ( e iew[Ti le/
Abs ac ] OR su ey[Ti le/Abs ac ] OR
benchma k*[Ti le/Abs ac ]) AND (bioin o ma ics[MeSH
Te ms] OR ”gene exp ession”[Ti le/Abs ac ])) AND
(”2009/01/01”[Da e - Publica ion] : ”3000”[Da e -
Publica ion])
Scopus que y (Big Da a-based biclus e ing algo i hms):
(TITLE-ABS-KEY(biclus e ing OR ”co-clus e ing”)
AND TITLE-ABS-KEY(”Big Da a” OR ”Spa k” OR ”Apache
Spa k” OR ”Hadoop” OR ”Apache Hadoop” OR ”MapReduce”
OR ”Map-Reduce”) AND TITLE-ABS-KEY(bioin o ma ics OR
”gene exp ession”)) AND PUBYEAR> 2008
Google Schola que y (GPU-based biclus e ing algo i hms):
”biclus e ing” AND (”GPU” OR ”CUDA” OR ”GPGPU” OR
”OpenCL”) AND (bioin o ma ics OR ”gene exp ession”)
- e iew -su ey a e :2008
As can be seen, each que y was ca e ully c a ed o ma ch he syn ax and index-
ing s a egies o he co esponding pla o m. Ad anced il e s (e.g. publica ion yea
ange, language, pee - e iewed jou nals) we e also applied when a ailable.
2.2 Eligibili y c i e ia andsc eening p ocess
A icles we e managed using Zo e o, which enabled deduplica ion and s uc u ed
agging. The selec ion was ca ied ou in wo s ages: (1) i le and abs ac sc eening
and (2) ull- ex e alua ion. Sepa a e inclusion/exclusion c i e ia we e de ined o
each analysis.
Fo su ey analysis, s udies we e included i hey we e published e iews o su -
eys ha analysed biclus e ing echniques applied speci ically o biological da a.
P io i y was gi en o wo k ocused on bioin o ma ics o omics da ase s, pa icula ly
hose in ol ing gene exp ession analysis. S udies we e excluded i hey did no ela e
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o biological applica ions—such as e iews cen ed on ma ke ing o social ne wo k
da a—o i hey lacked pee - e iewed s a us (e.g. edi o ials o in o mal o e iews).
Fo he analysis o he HPC/Big Da a biclus e ing algo i hm analysis, a icles
we e included i hey p oposed o applied biclus e ing echniques wi hin pa al-
lel o dis ibu ed compu ing en i onmen s, including implemen a ions using GPU
o GPGPU accele a ion. Wo ks ha desc ibed applica ions wi hin Big Da a ame-
wo ks such as Apache Spa k o Hadoop we e also eligible. The s udies ha we e no
included in his analysis we e hose ha did no ha e any compu a ional implemen-
a ion, did no ocus on scalabili y o pe o mance, o only used simple oy da ase s
ha we e no ele an o p ocessing la ge-scale biological da a.
The i s analysis yielded 490 candida e e iew a icles, o which 24 me all inclu-
sion c i e ia. The second analysis e ie ed 432 implemen a ion a icles, o which 24
we e selec ed a e ull- ex sc eening.
2.3 Da a ex ac ion andclassi ica ion
Fo bo h analyses, me ada a we e ex ac ed, including publica ion yea , ci a ion
coun ( ia Scopus/Google Schola ), jou nal qua ile, a ge o ganism (i applicable),
da a ype (e.g. mic oa ay, RNA-Seq), compu a ional pla o m (e.g. Spa k, MPI,
GPU), and e alua ion me ics.
S udies om he second analysis we e u he ca ego ised by compu a ional
pa adigm:
• Pa allel and dis ibu ed compu ing (e.g. MPI, OpenMP)
• Big Da a amewo ks (e.g. Spa k, Hadoop)
• GPU-based app oaches (e.g. CUDA, OpenCL)
This classi ica ion enabled a compa a i e syn hesis o algo i hmic s a egies and
implemen a ion pa e ns.
2.4 Re iew scope and ype
This wo k combines bo h sys ema ic and na a i e e iew elemen s. The sys em-
a ic componen s p o ide a ep oducible s uc u e and empi ical ounda ion, while
he na a i e analysis enables c i ical in e p e a ion o ends, bo lenecks, and me h-
odological gaps. This hyb id app oach allows o bo h e idence-based syn hesis and
o wa d-looking insigh .
2.5 E idence o in e na ional in e es and ele ance
In bo h analyses, he a icles included co e mo e han 20 coun ies, wi h s ong ep-
esen a ion om ins i u ions in he USA, China, Ge many, India, and Spain. App ox-
ima ely 74% o he selec ed s udies we e published in jou nals anked in Q1/Q2 in
hei ca ego y acco ding o he Scimago Jou nal Rank (SJR). I is also in e es ing o
no e ha o hese publica ions anked in Q1, he selec ed su eys exceed he 80%
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1123 Page 8 o 52
h eshold, while o HPC biclus e ing algo i hms hey s and a 67%. Ci a ion me ics
also suppo he ele ance o his ield: he a e age ci a ion coun was 47.3, wi h a
median o 31. These indica o s ein o ce he g owing in e na ional in e es in scal-
able biclus e ing echniques in biomedical esea ch.
3 Rela ed wo k
Nume ous biclus e ing e iews a e a ailable in he li e a u e, wi h he mos el-
e an ones de ailed in Table2 o Appendix A. This able includes publica ion yea ,
au ho s, p incipal applica ion, and i s scope: biclus e ing echniques (me hods) and
alida ion echniques ( alida ion). Re iews can span mul iple ca ego ies, as he
g oups a e no mu ually exclusi e. Rega ding biclus e ing s a egies, S. Busygin
e al. [20] o e an ex ensi e analysis o he ma hema ical p inciples in ol ed in he
sea ch o biclus e s by means o a ious echniques ha a e classi ied based on
hei applica ion domains. B. Pon es e al. [21] classi y algo i hms in o me ic and
non-me ic g oups, employing di e se e alua ion me ics such as i e a i e g eedy
sea ch, me a-heu is ics, clus e ing-based, p obabilis ic models, and linea algeb a.
Recen wo ks ha e been published ha p o ide a mo e comp ehensi e analysis, such
as he s udy by A. José-Ga cía e al. [22], which speci ically examines biclus e -
ing algo i hms ha use me a-heu is ics as e alua ion me ics. Conce ning e iews o
biclus e alida ion echniques, Tanay e al. [23] examine sco ing model in luence in
he de ec ion o signi ican biclus e s, while San ama ia e al. [24] p opose me ics
o illus a e he consis ency and e icacy o biclus e ing echniques o ge biological
in o ma ion in he p esence o noise exposu e and o e lap be ween biclus e s.
Al hough he a o emen ioned a icles p o ide heo e ical pe spec i es on biclus-
e ing app oaches, hey ail o add ess he issues posed by biological da ase s and
compu a ional obs acles. Thus, addi ional e alua ions seek o acqui e knowledge by
conduc ing compa a i e analyses o a ious me hodologies. Fo example, E en e al.
[25] ound ha he compu a ional e iciency o biclus e ing app oaches declines
wi h la ge gene exp ession da ase s and mo e biclus e s. Addi ionally, no all me h-
ods can gene a e biologically en iched biclus e s. Padilha e al.’s wo k [26] ei e -
a ed he indings o E en e al. [25] and p o ided u he de ails on he ac o s ha
in luence compu a ional e iciency, including noise in gene exp ession da a, biclus e
o e lap, size, and quan i y. Also, K. Nicholls e al. [27] compa ed a ious biclus e -
ing echniques, no ing ha adap i e algo i hms demons a e supe io compu a ional
pe o mance. They also obse ed a co ela ion be ween noise, da ase size, biclus e
coun , and dec eased compu a ional e iciency. Mo e ecen ly, Cas anho e al. [28]
ha e o e ed ano he s udy in which hey in oduce a uni ied axonomy o biclus e -
ing concep s and de ail a comp ehensi e analysis p ocess. Al hough hey iden i y
h ee main implemen a ion s a egies o big da a biclus e ing algo i hms—pa allel
compu ing, GPU-based execu ion, and MapReduce— hey limi he discussion o
whe he each me hod is a no el p oposal o an adap a ion o exis ing algo i hms.
Based on he highligh s om he a o emen ioned e iews, i can be de e mined
ha biclus e ing e o s ha e long been ying o add ess he limi a ions and imp o e
he quali y o he esul s. Howe e , e ining me a-heu is ics and add essing noise/
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o e lap challenges may no ully ex ac pe inen biological insigh s om as
da ase s. Likewise, cu en biclus e alida ion me hods may lack adequacy when
applied in Big Da a ecosys em. Rega ding hese conce ns, Xie e al. [6] p esen a
compelling s udy on he impac o biclus e ing echniques in cu en biomedical
con ex s. They highligh he lack o biclus e ing algo i hms ailo ed o la ge gene
exp ession da ase s wi h high complexi y. The au ho s ad oca e o he de elopmen
o no el biclus e ing me hods capable o managing biological and biomedical analy-
ses wi hin Big Da a en i onmen s. Also, he e a e o he wo ks ha a e no e iews
bu ha iden i y such p oblems. Fo example, in [29], he au ho s ocus on Big Da a
biclus e ing app oaches using HPC amewo ks like Apache Hadoop and Spa k,
along wi h pa allel sys ems wi h GPUs, o add ess scalabili y challenges. T adi ional
biclus e ing me hods a e deemed imp ac ical o Big Da a asks due o hei inabil-
i y o analyse da a e icien ly. The au ho s ou line key challenges, including le e ag-
ing pa allelism, unde s anding ha dwa e limi a ions, and add essing he inc ease in
biclus e quan i y and size wi h da a olume. They ecommend employing mul iple
alida ion measu es when assessing biclus e ing pe o mance in noisy en i onmen s
o wi h simila biclus e s. Addi ionally, hey cau ion agains o e -in e p e ing s a-
is ical signi icance in gene en ichmen and pa hway analysis, ad oca ing o addi-
ional il e ing o compa ison in Big Da a biclus e ing app oaches.
As he analysis o cu en li e a u e e iews on biclus e ing echniques shows,
many p oposals examine hese echniques om a ious pe spec i es. Howe e , none
o hese wo ks add esses he challenges posed by he big da a en i onmen , such as
p ocessing eno mous amoun s o da a and e alua ing a la ge numbe o biclus e s.
This a icle aims o add ess his gap by p o iding a comp ehensi e s udy o biclus-
e ing echniques om he pe spec i e o high-pe o mance compu ing (HPC).
3.1 C i ical analysis o p e ious wo k andiden i ied esea ch gaps
Al hough a signi ican numbe o challenges ha e con ibu ed o he de elopmen
o biclus e ing me hods o bioin o ma ics and Big Da a en i onmen s, se e al
limi a ions and esea ch gaps pe sis . Fi s , many adi ional biclus e ing algo i hms
exhibi limi ed scalabili y when applied o la ge-scale omics da ase s. These me h-
ods a e o en implemen ed in sequen ial en i onmen s and lack e icien adap a-
ions o dis ibu ed o pa allel compu ing amewo ks. Second, he in e p e abili y
o he esul ing biclus e s is equen ly o e looked, which es ic s hei usabili y in
biomedical con ex s whe e he explainabili y o compu a ional esul s is c ucial o
domain expe s. Ano he ecu en issue is he absence o s anda dised e alua ion
me hodologies; he di e si y o me ics and alida ion s a egies employed ac oss
s udies hampe s ai compa isons and ep oducibili y.
Mo eo e , he e is a gene al lack o in eg a ion o p io biological knowledge
in o he biclus e ing p ocess. Gene on ology e ms, known pa hways, and egula-
o y in e ac ions a e o en igno ed, despi e hei po en ial o guide mo e biologically
meaning ul clus e ing. Fu he mo e, while mode n machine lea ning pa adigms
such as deep lea ning and ensemble lea ning ha e shown success in o he bioin-
o ma ic asks, hei applica ion in biclus e ing emains limi ed. The ep oducibili y
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exp ession ma ix in o chunks co esponding o a ailable p ocesses. Each p o-
cess is esponsible o cons uc ing local biclus e s om i s alloca ed da a chunk,
while he main p ocess in eg a es hese biclus e s o p oduce he inal ou pu . As
da a ansmission among p ocesses is a oided du ing local biclus e cons uc ion,
he au ho s ind he communica ion cos o be negligible. Howe e , hey no e ha
he me hod’s bo leneck a ises om cons uc ing biclus e s wi h a single p ima y
p ocess, which can signi ican ly a ec compu ing pe o mance, especially wi h
excep ionally la ge da ase s. To alida e hei app oach, he au ho s employ a
syn he ic da ase measu ing 12651 x 30 o gene a e 250 biclus e s and claim simi-
la esul s o a yeas gene exp ession da ase [76], hough no suppo ing da a is
p o ided.
Liu e al. [77] in oduce P-Biclus e , a biclus e ing me hod designed o cons uc -
ing shi ing biclus e s [78] om gene exp ession da ase s con aining bo h con inu-
ous and disc e e alues. The echnique ga he s shi pa e ns in o a 2x2 ma ix and
u ilises he de ia ion om each pa e n o de e mine he shi end. Biclus e s a e
hen gene a ed by inc emen ally adding ows and columns o each pa e n un il hei
o se alue alls below a p ede ined h eshold. The au ho s e alua e hei app oach
using eal gene exp ession a ays [79] wi hou noise con ol and compa e esul s
wi h o he sequen ial me hods, assessing biclus e quali y based on de ia ion. How-
e e , alida ion lacks suppo om echniques ensu ing he biological signi icance
o biclus e s. In e ms o compu a ional p ope ies, P-Biclus e employs MPI o
pa allel compu ing on CPU clus e s. The au ho s obse e imp o ed compu a ional
pe o mance wi h se e al p ocesses up o se en bu no e ha pe o mance declines
beyond his h eshold due o a lack o con ol o e dis ibu ed memo y a chi ec u e
and p ocess communica ions. Hence, he me hod is ine ec i e o con inuous da a
ans e o la ge da a olumes. Addi ionally, he s udy lacks de ails on pa allelised
asks, CPU clus e u ilisa ion, synch onisa ion, and da a dis ibu ion, among o he
aspec s.
A. Nisa e al. [80] p esen a scalable and dis ibu ed algo i hm de i ed om
Ahmad e  al.’s wo k [81], which e o mula es he Bipa i e Spec al Pa i ion-
ing echnique [82] using g aph acing o achie e op imal solu ions. Thei me hod
in ol es posi ioning each node o he bipa i e g aph a he ba ycen e o i s neigh-
bou s o iden i y op imal solu ions. The biclus e ing p ocess consis s o wo pa allel
asks: c osso e minimisa ion and biclus e iden i ica ion. Ini ially, he gene exp es-
sion da ase is ho izon ally spli in o homogeneous chunks, each assigned o an
a ailable p ocess. Da a no malisa ion is pe o med, ollowed by i e a i e ow (local)
and column (global) eo de ing. Each p ocess conduc s a local sea ch o biclus-
e s, and ep esen a i e biclus e s a e globally dis ibu ed. The au ho s employ MPI/
C++ o accele a e compu a ional pe o mance and analyse algo i hmic complex-
i y and memo y a chi ec u e managemen . They no e ha MPI-based biclus e ing
algo i hms incu communica ion cos s as da ase size g ows bu claim independence
om ow coun by using c osso e minimisa ion. They demons a e scalabili y by
analysing syn he ic da ase s wi h up o 20 million ows and 64 ixed columns and
using up o 256 p ocesso s. Howe e , he s udy lacks analysis o eal gene exp es-
sion da ase s and compa ison wi h o he me hods o compu a ional accele a ion
echniques.

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MFCM [83] is a biclus e ing echnique de i ed om he C-Means uzzy clus-
e ing algo i hm [84] o gene a ing biclus e s om gene exp ession da ase s. The
me hod u ilises Ma labMPI and an SPMD pa allel compu ing model o dis ibu e
compu a ional wo kload ac oss mul iple p ocesso s. The da ase is di ided in o
chunks co esponding o MATLAB p ocesses, ollowed by gene and sample clus e -
ing o cons uc biclus e s. Gene cen es a e de e mined based on en opy a he han
co ela ion coe icien s, wi h i e a ions con inuing un il a h eshold o ejec ed genes
o a maximum i e a ion limi is eached. Real gene exp ession da ase s a e used,
compa ing esul s o a sequen ial e sion in MATLAB. Compu a ional pe o mance
imp o es a all s eps compa ed o he sequen ial e sion, bu challenges may a ise
wi h inc eased p ocesses o la ge da ase s due o communica ion delays. The s udy
lacks conside a ion o challenges in da a dis ibu ion, memo y a chi ec u e manage-
men , and da a ans e s ac oss p ocesses, and i only e alua es up o eigh p ocesses,
lea ing pe o mance unce ain y wi h mo e.
The Bioconduc o unibic package [85] adap s he UniBic biclus e ing echnique
[86] o pa allel compu ing o gene a e meaning ul biclus e s om gene exp ession
da a. The modi ica ion aims o accommoda e high- h oughpu RNA-Seq, scRNA-
Seq, and PCR da ase s, imp o ing un imes o la ge da ase s wi h hund eds o
housands o columns and housands o ows. The au ho s p o ide de ailed compu-
a ional decisions o pa allelising asks, excep o ma ix disc e isa ion. They e-
implemen he algo i hm in C++11 wi h he OpenMP s anda d o enhanced pe -
o mance. Res uc u ing he sou ce code op imises ea u es and add esses memo y
segmen a ion issues. OpenMP is u ilised o asks like compu ing he Longes Com-
mon Sequence (LCS) [87] be ween ow pai s, dis ibu ed ac oss CPU co es. How-
e e , e idence on he algo i hm’s pe o mance wi h huge da ase s and he a ionale
behind OpenMP usage is lacking. Consequen ly, he scalabili y and pe o mance o
he OpenMP implemen a ion depend on single-machine ha dwa e.
González-Dominguez e  al. [88] de eloped Pa BiBi , a pa allel e sion o he
BiBi algo i hm [89], designed o ex ac biclus e s om bina y da ase s. Pa BiBi ,
implemen ed in C++11, uses MPI and POSIX h eads o exploi bo h sha ed and
dis ibu ed memo y in a hyb id clus e en i onmen . Tasks wi h high compu a ional
cos s, such as pa e n cons uc ion and ow agg ega ion, a e iden i ied. MPI and
POSIX h eads dis ibu e he wo kload ac oss CPUs and nodes o enhance compu-
a ional pe o mance. Howe e , da a ans e s be ween p ocesses may sa u a e he
ne wo k, impac ing pe o mance. López e  al. [90] demons a ed Pa BiBi ’s deg-
ada ion in pe o mance wi h dense da ase s and iden i ied memo y managemen
conce ns o la ge da ase s. F aguela e al. [91] add essed hese issues wi h ScalaPa-
BiBi , also using C++11, MPI, and POSIX Th eads. Sequen ial op imisa ions and
me hodological weaks we e applied o enhance compu a ional speeds and suppo
la ge da ase s. The main di e ence lies in ScalaPa BiBi ’s app oach o gene a ing
pa e ns; i aims o dis ibu e pa e n se s o op imise biclus e elabo a ion. Howe e ,
noise sensi i i y in da ase s emains unadd essed ac oss all e sions.
The COBRAC Con ex biclus e ing algo i hm [92] is capable o iden i ying
po en ial biclus e s along wi h he associa ed ow and column clus e ing dend o-
g ams. This algo i hm ope a es by educing he inpu da ase size and sol ing he
con ex weigh ed biclus e ing p oblem. The u ilisa ion o con ex clus e ing ees
A.López-Fe nández e al.
1123 Page 18 o 52
ensu es obus ness agains mino inpu a ia ions [93]. COBRAC is implemen ed
in C/C++ o he algo i hm i sel and Py hon con aine s o b oade accessibili y.
OpenMP is employed o accele a e he weigh ed con ex biclus e ing ask. While
educing he inpu da ase size imp o es execu ion ime and enables handling la ge
biological da ase s, u he compu a ional documen a ion is needed o elucida e hei
esul s and esou ce usage. Addi ionally, he a ionale behind choosing OpenMP
should be cla i ied, especially ega ding i s dependence on single-machine ha d-
wa e. Al hough he au ho s p io i ise imp o ing usabili y, unning simula ions on
a machine wi h 512 GB o RAM may no e lec ypical compu ing en i onmen s.
Fu he explo a ion wi h la ge da ase s is wa an ed o e alua e he algo i hm’s
scalabili y and pe o mance impac .
The ARBic algo i hm de eloped by Ma e al. [94] is an accele a ed ule-based
biclus e ing algo i hm designed o iden i y ele an gene condi ion pa e ns in bio-
medical da ase s. Implemen ed in C++, ARBic employs OpenMP o pa allelise
he expansion phase o seed biclus e s, which is compu a ionally in ensi e due o
he high dimensionali y o omics da a. OpenMP enables ARBic o exploi sha ed
memo y pa allelism by dis ibu ing he p ocessing o candida e seeds ac oss mul-
iple h eads. This pa allel s a egy signi ican ly educes execu ion ime du ing he
explo a ion o he biclus e sea ch space. Howe e , he use o OpenMP con ines he
algo i hm o sha ed memo y a chi ec u es, limi ing scalabili y o la ge da ase s ha
exceed he memo y capaci y o a single node. Fu he mo e, he inse ion o esul s
in o sha ed s uc u es equi es c i ical sec ions, which may in oduce synch onisa-
ion o e head and limi pa allel e iciency. To enhance ARBic’s pe o mance, u u e
e sions could adop hyb id pa allelism by in eg a ing OpenMP wi h MPI o ask-
based un ime sys ems, allowing dis ibu ed memo y suppo and mo e e ec i e
wo kload balancing. Addi ionally, op imising memo y access pa e ns and educing
con en ion in sha ed da a s uc u es could u he imp o e scalabili y and pa allel
h oughpu .
B. Ba uah e al. [95] in oduce EnsemBic, an assembly-based biclus e ing me hod
ha in eg a es se e al me hods (Laplace P io , iBBiG, and xMo i ) o ex ac unc-
ionally ele an biclus e s om gene exp ession da a. The me hod iden i ies high-
quali y biclus e s using p- alues de i ed om FuncAssocia e 3.0 and employs an
i e a i e app oach o elimina e gene ic simila i ies wi hin biclus e s. The au ho s
emphasise ha he design acili a es he execu ion o base algo i hms in pa allel,
po en ially dec easing compu a ion ime; howe e , he R implemen a ion does no
explici ly inco po a e pa allelisa ion echniques such as OpenMP, MPI, o na i e R
lib a ies o pa allel compu ing. This seeming lexibili y indica es ha pa allelisa-
ion is ei he ex e nally managed o subjec o use implemen a ion. EnsemBic has
been assessed using ou au hen ic da a se s ( wo mic oa ays and wo RNA-seq),
demons a ing consis en enhancemen s o e s andalone me hods in bo h opologi-
cal me ics (in e nal densi y, modula i y, ODF) and biological me ics (GO en ich-
men , pa hway analysis, and alida ion by ChIP-seq). Mo eo e , he inclusion o he
p- alue as a quali y c i e ion ende s he app oach sensi i e o non-signi ican pa -
e ns and enhances i s obus ness agains noise. The algo i hm unc ions on eal con-
inuous da a; howe e , i s use wi h syn he ic, discon inuous, o bina y da a has no
been documen ed.
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The e a e addi ional biclus e ing echniques ha a e accele a ed using con en-
ional pa allel models, bu insu icien in o ma ion is a ailable due o he una ail-
abili y o so wa e o a lack o desc ip ion ega ding he pa allel model and com-
pu a ional esou ces. One such example is PBD-SPEA2 [96], an adap a ion o
he SPEA2 mul i-objec i e e olu iona y algo i hm [97] o gene exp ession da a.
PBD-SPEA2 uses a ixed-leng h, in ege -based encoding o e alua ing biclus e
se s. Objec i e unc ions include MSR [14], BSize [22], and VAR [98], wi h he
Cheng–Chu ch [14] heu is ic o p oducing new solu ions. Pa allel compu ing is
applied o he dynamic coding scheme ask, al hough speci ic pa allelisa ion me h-
ods, s anda ds, o models a e no p o ided, no is he algo i hm’s sou ce code a ail-
able, hinde ing u he explo a ion o compu a ional ea u es.
In MPI-based biclus e ing algo i hms, he size and di e si y o inpu da ase s play
a c ucial ole in speeding up compu ing pe o mance. La ge da ase s o en lead o
memo y managemen and da a ans e challenges due o communica ion sa u a ion
among memo y egions ac oss p ocesso s in he clus e . ScalaPa BiBi o e s a solu-
ion o hese limi a ions. On he o he hand, biclus e ing algo i hms using OpenMP
ace limi a ions inhe en in his implemen a ion, which p ima ily pa allelises p o-
g ams in a sha ed memo y en i onmen , a oiding communica ion o e head bu
es ic ing hem o a single compu e ’s ha dwa e esou ces. The COBRAC algo i hm
exempli ies hese cons ain s by comp essing da ase size o educe execu ion ime
compa ed o i s sequen ial e sion. Addi ionally, conside a ions such as ask selec-
ion o pa allelisa ion, a chi ec u e, and memo y managemen mus be add essed.
While algo i hms may ace limi a ions due o ha dwa e esou ces, such an app oach
ensu es scalabili y wi hou o e loading o memo y o e low. Fu he mo e, as da a-
se olume inc eases, he likelihood o ob aining mo e biclus e s ises, equi ing
ca e ul conside a ion du ing p ocessing and memo y managemen .
5.2 Pa allel biclus e ing wi hbig da a p og amming pa adigms
The eme gence o Big Da a p og amming pa adigms add esses he limi a ions o
adi ional pa allel and dis ibu ed models in handling la ge da ase s e icien ly. Con-
sequen ly, p og amming pa adigms like MapReduce, along wi h pla o ms such as
Apache Hadoop, Apache Spa k, and MATLAB, o e solu ions o massi ely pa -
allel o dis ibu ed biclus e ing. Two ables p esen he cha ac e is ics o he ana-
lysed biclus e ing me hods o you e e ence. Table6 o Appendix A ou lines he
cha ac e is ics o biclus e ing echniques ailo ed o a Big Da a en i onmen . The
i s column lis s he names o he biclus e ing algo i hm o au ho s, i no speci ied.
The second column indica es he Big Da a pla o m used. The ypes o da ase s used
in expe imen s a e de ailed in he hi d column: 0 o syn he ic da ase s, 1 o eal
da ase s, and 2 o a combina ion o bo h. The ou h column deno es whe he com-
pa isons we e made wi h o he s udies: 0 o no compa ison, 1 o compa isons wi h
sequen ial me hods, and 2 o compa isons wi h o he Big Da a-adjus ed biclus e -
ing algo i hms o deploymen on di e en Big Da a pla o ms. The i h column
no es he algo i hm’s abili y o handle noise. Da a ypes used in expe imen s a e
A.López-Fe nández e al.
1123 Page 20 o 52
lis ed in he six h column, acknowledging ha addi ional da a ypes migh also be
compa ible.
Table7 o Appendix A delinea es he compu a ional cha ac e is ics o he dis-
cussed biclus e ing me hods. The i s column p esen s he algo i hm’s name o
au ho s’ names. The second column indica es he numbe o MR jobs equi ed o
gene a ing esul s, whe e 0 signi ies a small numbe and 1 indica es a highe num-
be . The hi d column deno es he me hod’s capabili y o da a spli ing, c ucial o
balancing wo kload and da a ans e s [99]. The ou h column indica es whe he
me hods ha e s a egies o minimise I/O ope a ions, which is c i ical o pe o -
mance, especially in Apache Hadoop se ups. The i h column speci ies i in e medi-
a e da a is s o ed in main memo y o in-memo y compu a ion, educing disk I/O.
The six h column iden i ies whe he MR jobs a e asynch onous, impac ing ha dwa e
and so wa e esou ce u ilisa ion. The se en h column e alua es he in es iga ion o
op imisa ion s a egies, which a e c ucial o e icien pa allelism. The inal column
indica es he ype o scalabili y expe imen s conduc ed: 1 o inc easing da a ol-
ume, 2 o aising he numbe o clus e nodes, and 3 i bo h scalabili y es s we e
pe o med.
BiTM-MR [100] is an ea ly biclus e ing app oach using he MapReduce pa a-
digm, implemen ed wi h Apache Spa k o ex ac ing biclus e s om massi e da a-
se s. The me hod ans o ms da a ma ices in o a block s uc u e o ganised as a opo-
logical map and employs wo bina y ma ices o alida e ow–column ela ionships.
O e coming challenges like da a loading, ailu e sa e y, and algo i hm design, he
au ho s use Apache Spa k o aul co ec ion, da a dis ibu ion, and managemen .
The me hod uses wo MR jobs o i e a e ows and columns and adjus pa ame e s.
The au ho s signi ican ly imp o e compu a ional e iciency by alloca ing independ-
en MR jobs o ows and columns, minimising I/O ope a ions, and enabling asyn-
ch onous p ocessing. Howe e , de eloping a da a pa i ioning echnique such ha
each MR job akes ca e o a g oup o homogeneous ows and columns is an addi-
ional compu ing ac o ha he au ho s ha e no accoun ed o and ha could be
use ul o enhancing hei algo i hm design. Syn he ic da ase s wi h up o wo mil-
lion ows demons a e nea -ideal compu a ional e iciency as da ase size and clus e
co es inc ease. Howe e , compa isons wi h o he biclus e ing algo i hms o pe o -
mance accele a ion esou ces would ha e added alue. The au ho s no e ha Apache
Spa k ini ialisa ion impac s pe o mance mo e on smalle da ase s.
Ruiqi e al. [101] adap ed he non-nega i e ma ix ac o isa ion biclus e ing algo-
i hm [102] using Apache Hadoop o handle 2D spa se ma ices up o one million
by one million. Thei app oach equi es a non-nega i e ma ix and a epe i ion coun
as inpu , using i e MR jobs pe i e a ion o upda e each ou pu subma ix. Howe e ,
his app oach’s hea y eliance on MR jobs may impac compu a ional pe o mance
on la ge da ase s due o equen disk access cos s. Conside a ions such as educ-
ing I/O ope a ions, op imising da a pa i ioning, and enabling asynch onous com-
munica ion be ween map and educe unc ions we e no add essed. The s udy sug-
ges s oom o signi ican imp o emen in bo h compu a ional cos and scalabili y.
An e alua ion wi h syn he ic da ase s and a da ase exceeding one million ows and
columns om he STRING da abase [103] was conduc ed, analysing compu a ional
e iciency conce ning he numbe o nonze o in e ac ions. Howe e , he e was no
Biclus e ing inbioin o ma ics using big da a andHigh… Page 21 o 52 1123
analysis o how well i scales wi h mo e Hadoop clus e nodes o how i compa es o
o he biclus e ing me hods o di e en esou ces o handling la ge da ase s.
MR-GABiT [104] is designed o gene ic biclus e ing o ime se ies da ase s om
mic oa ay expe imen s. Despi e he da ase s being h ee-dimensional, he au ho s
apply biclus e ing o ex ac local pa e ns a di e en ime poin s, which a e hen
compa ed o de e mine op imal pa e ns. Using he MATLAB MapReduce ame-
wo k, hey add ess dimensionali y and scalabili y issues. Da a pa i ioning by ime
poin s educes MR job coun , imp o ing compu a ional pe o mance. Each MR ask
employs a map unc ion o ex ac he bes indi idual om da a chunks, wi h educe
unc ions combining hese bes indi iduals. Howe e , limi a ions include he inabil-
i y o educe disk I/O ope a ions and educe unc ions wai ing o all map unc ions
o comple e. E alua ion wi h yeas cell cycle ime se ies da ase s [105] is conduc ed,
bu he scalabili y wi h inc eased clus e nodes is no es ed, and he e is no com-
pa ison wi h o he gene ic biclus e ing me hods o Big Da a esou ces.
The e a e addi ional biclus e ing s udies using he MapReduce pa adigm whe e
compu a ional de ails a e insu icien and he so wa e is una ailable. One such wo k
is MCC [106], an adap a ion o he Cheng–Chu ch (CC) biclus e ing echnique [14].
MCC andomly gene a es nume ous unique suba ays and execu es mo e compu a-
ions on hem han he o iginal CC algo i hm, making i compu a ionally in ensi e.
While MapReduce is used, speci ics on i s adap a ion, da a pa i ioning, and dis i-
bu ion a e no p o ided. Real gene exp ession da ase s show ha MCC wo ks be -
e han he sequen ial CC algo i hm in e ms o accu acy and speed. The au ho s
acknowledge MCC’s ine iciency in memo y managemen due o he need o s o e
nume ous in e media e a ays and he high numbe o I/O ope a ions.
As can be seen, his subsec ion highligh s he sca ci y o bioin o ma ics- ela ed
biclus e ing algo i hms on Big Da a pla o ms like Spa k o Hadoop. Consequen ly,
his subsec ion also includes biclus e ing algo i hms ha ha e been c ea ed in disci-
plines o science un ela ed o bioin o ma ics bu ha a e based on he MapReduce
pa adigm. Then, i is possible o c ea e ais as o whe he he lack o algo i hms is
due o he complexi y o he biclus e ing echnique’s de ini ion o o scien i ic ield-
speci ic ac o s in bioin o ma ics. DisCo [107] ea anges subma ix ows and col-
umns o mee a quali y h eshold, using key– alue pai s o s o e ows. Bha naga
e al. [108] de eloped a MapReduce biclus e ing echnique using o mal concep
analysis (FCA) o bina y da ase s [109]. Thei ocus is on imp o ing compu a ional
pe o mance as da ase size inc eases, al hough hey ha e no explo ed scalabili y
wi h node numbe s, and he BiBi sequen ial biclus e ing me hod achie es as e
execu ion speeds in eigh o he ou een o e all pe o mance e alua ions. Lin e al.
[4] de eloped biclus e ing algo i hms o he elecommunica ions ield, wi h e -
sions o Spa k (SP-PLSS) and Hadoop (MR-PLSS). They aim o iden i y p o i able
cus ome s wi h simila buying beha iou s, esul ing in be e e iciency and scalabil-
i y wi h Apache Spa k o la ge da ase s. This analysis demons a es ha , ega dless
o he ield o s udy, biclus e ing echniques ha e no been widely implemen ed on
Big Da a pla o ms such as Apache Spa k and Apache Hadoop. In addi ion, he e is
a signi ican gap be ween he p esen numbe o biclus e ing algo i hms p oduced on
hese Big Da a pla o ms and o he ypes o compu a ional accele a ion applica ions.
As a esul , mul iple hypo heses may eme ge om di e se iewpoin s.

A.López-Fe nández e al.
1123 Page 22 o 52
Many adi ional biclus e ing me hods need o cons an ly p ocess one pa o
he da ase , which makes i di icul o design hese me hods [14, 89, 94, 110, 111].
Consequen ly, no all s a egies easily adap o he MapReduce pa adigm due o his
complexi y. The algo i hm design mus conside ac o s like MR job coun , da a-
se pa i ioning, minimising I/O ope a ions, in-memo y sys ems, and asynch onous
communica ion be ween map and educe unc ions. P ocessing la ge da ase s can
g ea ly enhance pe o mance, bu he di e ences in bioin o ma ics da ase s migh
make i ha d o hese algo i hms o wo k well, especially when he e a e only a ew
genes and expe imen al condi ions [108]. Also, Big Da a pla o m ini ialisa ion ime
impac s pe o mance [49, 100], and issues like educe unc ions wai ing o map
unc ion comple ion and uncon olled execu ion o de can deg ade pe o mance.
Biclus e ing algo i hms on Apache Spa k ou pe o m hose on Apache Hadoop due
o in e media e I/O ope a ions elying on memo y a he han disk [4].
5.3 Pa allel biclus e ing onGPUs
The GPGPU concep ha nesses he p ocessing esou ces o GPU de ices o accel-
e a e compu a ional pe o mance, making hem iable al e na i es o supplemen s
o CPU p ocessing o biclus e ing algo i hms [112]. Howe e , biclus e ing algo-
i hms ailo ed o GPGPU mus conside a ious ac o s ou lined in subsec ion4.3
o op imise compu a ional e iciency and suppo Big Da a da ase s. These ac o s
a e analysed o each biclus e ing algo i hm adap ed o GPGPU and p esen ed in
wo ables. Table8 o Appendix A p o ides key ea u es o biclus e ing echniques
implemen ed on GPGPU. The i s column lis s algo i hm names, while he second
column indica es da ase ypes: 0 o syn he ic, 1 o eal, and 2 o bo h. The hi d
column indica es i compa isons we e made wi h o he me hods: 1 o sequen ial
me hods and 2 o o he GPGPU-adap ed algo i hms. The ou h column no es i
he app oach handles da ase noise. The i h column speci ies he da a ypes used,
acknowledging ha o he ypes may also be applicable. The inal columns de ail
suppo ed da a ypes o each algo i hm.
Table9 o Appendix A de ails he compu a ional aspec s o biclus e ing algo-
i hms. The i s column lis s algo i hm names. The second column speci ies mul i-
GPU suppo and he pa allelism echnique. Sha ed memo y usage is no ed in he
hi d column. The ou h column indica es he use o coalescence. The memo y allo-
ca ion me hod is speci ied in he i h column (0 o non-pinned, o he wise pinned).
The da a ans e me hod (global memo y o uni ied memo y) is desc ibed in he
six h column. Occupancy le els, e lec ing esou ce u ilisa ion, a e in he se en h
column. To be e e lec he compu a ional oo p in o he su eyed ools, we clas-
si ied esou ce occupancy in o h ee ca ego ies based on es ima ed a e age usage o
compu a ional esou ces: low (<40%), medium (40–70%), and high (>70%). The
inal column indica es how synch onisa ion poin s a e used.
A nedo-Fe nández e al. [113] in oduced an adap a ion o he FLOC biclus e -
ing me hod [114], ocusing on he compu a ional in ensi y o calcula ing he MSR
measu e [14] o biclus e alida ion. U ilising CUDA o his ask, hey demons a e
imp o ed compu a ional pe o mance compa ed o he sequen ial e sion, using up
Biclus e ing inbioin o ma ics using big da a andHigh… Page 23 o 52 1123
o 2000 x 2000 squa e syn he ic da ase s. Howe e , hey did no ully exploi he
GPU’s capabili ies, no ing da a ans e ia he PCI-Exp ess bus as a bo leneck
and no conside ing mul i-GPU wo kload spli ing. The au ho s also discussed he
impac o h ead coun pe CUDA block on compu a ional e iciency, obse ing
diminishing e u ns wi h excessi ely high h ead coun s. Rega ding memo y man-
agemen , he au ho s do no speci y he a ious o ms o memo y p esen in a GPU
de ice. The e o e, i is assumed ha da a is always used om he GPU’s global
memo y.
Liu e al. [17] pa allelised he ask o p ocessing column pai s in he Geome -
ic Biclus e ing algo i hm (GBC) [115]. They de eloped h ee pa allel implemen-
a ions: one using POSIX Th eads, ano he using CUDA, and a hi d employing a
ield-p og ammable ga e a ay (FPGA) [116]. In he CUDA e sion, columns a e
pai ed pe CUDA block, wi h each h ead esponsible o combining a po ion o
each column using sha ed memo y o ensu e high coalescence and a oid memo y
con lic s. Real da ase expe imen s indica e ha he GPU e sion achie es he high-
es speedup, while he FPGA e sion is he mos ene gy-e icien . The au ho s dis-
co e ha minimising da a ans e s be ween RAM and GPU global memo y is c u-
cial, especially o la ge da ase s in he GPU e sion. In ano he wo k, Liu e al.
[117] apply he GBC algo i hm o iden i y neu al p ocessing pa e ns in mic oa ay
da ase s, p esen ing h ee CUDA e sions wi h dis inc op imisa ions. The i s e -
sion uses sha ed memo y o s o e column pai chunks, ensu ing no o e head ega d-
less o chunk size. A e loading he a ge column pai in o sha ed memo y, me ging
ope a ions a e conduc ed, and ou comes a e s o ed in global memo y. Mo eo e , he
chunking o column pai s p e en s he sha ed memo y om o e loading, necessi a -
ing a global synch onisa ion o e e y p ocessed chunk. Acco ding o he au ho s,
his global synch onisa ion incu s a cos by es o ing ine icien column pai s in he
global memo y. The second e sion a emp s ope a ions on mul iple column com-
bina ions o mi iga e his, while he hi d e sion ocuses on educing index upda e
ime. In o de o accomplish his, hey examine he e ec o con inuous ans e s
be ween GPU de ices and he CPU, a p oblem iden i ied in p io wo k [17]. Real
da ase expe imen s show he second e sion achie es he bes speedup, emphasis-
ing he impo ance o da a euse, minimising global memo y accesses, and dis ib-
u ing wo kloads be ween CPU and GPU o la ge da ase s. Despi e hese op imisa-
ions, none o hese e sions implemen s a mul i-GPU design, which could u he
enhance wo kload alloca ion and suppo o la ge da ase s.
Mejía-Roa e al. [118] in oduced a mul i-GPU adap a ion o he non-nega i e
ma ix ac o isa ion (NMF) [102] algo i hm, using he MPI s anda d o mul i-GPU
synch onisa ion. The algo i hm pa i ions he da ase in o blocks and dis ibu es i
ac oss GPUs, subsequen ly decomposing he da a in global memo y. A 1D block
se up achie es coalescence by accessing consecu i e memo y add esses. Asynch o-
nous CPU- o-GPU da a ans e s enhance compu a ional pe o mance. Real da ase
expe imen s con i m ha he GPU e sion ou pe o ms he sequen ial CPU-based
algo i hm, wi h speedup inc easing wi h da ase size. Scalabili y analysis e eals
ha ac o s like da a ans e s, synch onisa ion o e heads, and da ase size a e c i i-
cal in mul i-GPU designs [119]. Fo la ge da ase s, he mul i-GPU app oach is p e -
e able, while a single GPU su ices o smalle ones.
A.López-Fe nández e al.
1123 Page 24 o 52
The Condi ion-dependen Co ela ion Subg oups (CCS) [120] algo i hm, de el-
oped in CUDA, ully u ilises GPU de ices o biclus e gene a ion. Real and syn-
he ic da ase s demons a e a 20x speedup compa ed o he sequen ial implemen-
a ion. Howe e , unlike p e ious wo ks, he algo i hm does no employ da ase
chunking using CUDA block/ h ead decomposi ion, which limi s i s scalabili y o
he GPU memo y size. Sha ed memo y expedi es biclus e c ea ion bu may o e -
load wi h excessi e columns, capped a 200. Each CUDA block cons uc s a biclus-
e using a single h ead, esul ing in low occupancy. The me hod lacks suppo o
mul i-GPU a chi ec u es; hus, i does no explo e wo kload dis ibu ion o scalabil-
i y o la ge da ase s.
EBIC, de eloped by O zechowski e al. [121], is a pa allel e olu iona y biclus e -
ing me hod designed o disco e nume ous ele an pa e ns wi h high p ecision.
I uses a mul i-GPU a chi ec u e o dis ibu e da ase ows ac oss GPUs, handling
he en i e biclus e gene a ion p ocess. The au ho s op imise wo kload dis ibu ion
ac oss GPUs, CUDA blocks, and h eads by using sha ed memo y o compu ing
i ness unc ions and gene a ing biclus e s. Expe imen al esul s wi h syn he ic and
eal da ase s demons a e EBIC’s compu a ional e iciency, high coalescence, and
occupancy. EBIC ou pe o ms al e na i e echniques like CCS by up o 12 imes o
la ge da ase s. Howe e , he algo i hm’s scalabili y o Big Da a is limi ed by he
maximum suppo o 60000 ows pe GPU de ice.
González-Domínguez e al. [122] implemen ed CUBiBi , a mul i-GPU e sion
o he BiBi [89] biclus e ing algo i hm. CUBiBi employs GPU de ices o add
ows o po en ial biclus e s, wi h he emaining p ocessing done on he CPU. In he
case o a mul i-co e p ocesso , i can exploi he mul iple CPU co es while inding
po en ial biclus e s hanks o a pa allelisa ion wi h POSIX h eads. CuBiBi employs
he GPU’s sha ed memo y o s o e seeds o compu a ional pe o mance. Howe e ,
his decision limi s scalabili y o la ge da ase s because his memo y is iny and he
numbe o pa e ns inc eases as he da ase g ows. Fu he mo e, he o iginal me hod
ou pe o ms i o small da ase s. gBiBi [90], ano he e sion o BiBi , uses a mul i-
GPU a chi ec u e o p ocess massi e bina y da ase s e icien ly. The me hodology
add esses da a ans e , wo kload dis ibu ion, and esou ce u ilisa ion by ou pe -
o ming o he adap a ions and being he only modi ied e sion capable o handling
big da ase s. In ano he wo k, he au ho s c ea ed a Py hon package named bioSci-
ence ha uses HPC o speed up a ious da a mining echniques, such as he BiBi
algo i hm, using CPU and mul i-GPU clus e s [123].
The u ilisa ion o GPU de ices o accele a ing biclus e ing algo i hms has
become a p e alen end, wi h CUDA being he mos used pla o m o his pu -
pose. Va ious aspec s need conside a ion when de eloping and op imising biclus-
e ing algo i hms in a mul i-GPU en i onmen , as obse ed in he p e ious wo ks.
While GPGPU pa allel algo i hms gene ally ou pe o m sequen ial ones in e ms
o compu ing pe o mance, many s uggle o handle massi e da ase s e ec i ely.
In he majo i y o he analysed algo i hms, au ho s s ess he impo ance o plan-
ning esou ce alloca ion o GPU u ilisa ion. CCS, o ins ance, employs a CUDA
block o pa allelise a ask, whe eas CUBiBi , NMF, EBIC, and o he algo i hms use
he h eads o a CUDA block o do he pa allelisa ion ask. This decision is di ec ly
dependen on he algo i hm’s occupancy a e and, consequen ly, on whe he he
Biclus e ing inbioin o ma ics using big da a andHigh… Page 25 o 52 1123
implemen a ion will use he ull powe p o ided by he g id o each GPU de ice.
Fo algo i hms suppo ing mul i-GPU a chi ec u es, wo kload dis ibu ion among
de ices is ca e ully conside ed. These wo ks use pa allelisa ion echniques like
POSIX Th eads, MPI, and OpenMP o dis ibu e asks, bu he mos e ec i e s a -
egy emains unclea due o he a ie y o op ions a ailable.
Rega ding memo y- ela ed elemen s, he au ho s o se e al wo ks iden i y
CPU–GPU da a ans e as a pe o mance bo leneck due o communica ion bus lim-
i a ions, leading some o minimise ans e s. Con inuous global memo y access can
also impede e iciency, p omp ing he use o sha ed memo y o boos speed. How-
e e , imp ope sha ed memo y use, as seen in CuBiBi o CCS, can hinde pe o -
mance on la ge da ase s o smalle da ase s. Thus, sha ed memo y usage’s e icacy
a ies wi h da ase size and should be ca e ully assessed. Mos algo i hms employ
ixed memo y ese a ion and alloca ion s a egies. Synch onisa ion be ween GPU
de ices is c ucial in mul i-GPU sys ems, bu i a ies based on algo i hm echnique.
Fo example, NMF and gBiBi use asynch onous communica ions o minimise syn-
ch onisa ion and imp o e e iciency.
5.4 Pe o mance conside a ions ingene exp ession da a analysis
Implemen ing biclus e ing algo i hms using HPC echnologies in big da a en i on-
men s has shown a iable pe o mance depending on he speci ic cha ac e is ics o
he biological ask. In he con ex o gene exp ession da a analysis, which ypically
in ol es la ge-scale, high-dimensional, and spa se ma ices, each compu a ional
model p esen s dis inc ad an ages and limi a ions. Benchma k da ase s a e com-
monly u ilised o assess he pe o mance and scalabili y o HPC biclus e ing me h-
ods wi hin a Big Da a amewo k, ensu ing bo h e icacy and biological signi icance.
The Cance Genome A las (TCGA) and he Geno ype-Tissue Exp ession (GTEX)
p ojec [124] a e he mos equen ly u ilised esou ces. The TCGA o e s ex ensi e,
high-dimensional gene exp ession da a ac oss many cance ypes, se ing as a alu-
able esou ce o assessing he scalabili y and p ecision o biclus e ing algo i hms
in eal-wo ld biomedical con ex s. Con e sely, GTEX p o ides comp ehensi e
ansc ip ome da a om se e al non-diseased human issues, acili a ing he iden-
i ica ion o issue-speci ic gene co-exp ession modules. The ex ensi e u ilisa ion o
TCGA and GTEX s ems om hei public accessibili y and comp ehensi eness, as
well as he chance hey p o ide o e alua e algo i hmic esilience in high- h oughpu
se ings, which is c ucial o implemen ing biclus e ing app oaches wi hin HPC o
Big Da a amewo ks. Syn he ic da ase s a e u ilised in a ious esea ch o e alua e
algo i hm pe o mance unde con olled se ings o noise, size, and s uc u e.
Table 1 p o ides a compa a i e summa y o he main HPC pa adigms dis-
cussed in his sec ion, ou lining hei associa ed echnologies, s eng hs, limi a-
ions, and ypical applica ions in gene exp ession biclus e ing asks. T adi ional
pa allel and dis ibu ed models, such as MPI-based clus e s, a e p o icien a
managing la ge-scale gene exp ession da ase s, especially in jobs cha ac e ised
by signi ican da a pa allelism, such as gene o sample il e ing. Ne e heless,
hei e icacy may diminish in i e a i e biclus e ing con ex s due o in e -node
A.López-Fe nández e al.
1123 Page 32 o 52
Based on he esea ch conduc ed in he p eceding sec ions o his wo k, he p i-
ma y challenges ha biclus e ing mus o e come in o de o adap o a Big Da a
ecosys em a e ou lined below. These challenges a e ca ego ised in o ou p ima y
g oups: da ase s, biclus e ing app oaches, biclus e ing alida ion app oaches, and
isualisa ion and in e p e a ion o he esul s.
7.1 Da a‑cen ic challenges
Due o he exponen ial g ow h o biological and biomedical da a alongside ad ance-
men s in NGS [166] echnologies, bioin o ma ics is con on ed wi h signi ican
challenges in s o ing and analysing as da ase s. The pace o da a gene a ion om
sequencing ou paces he compu a ional esou ces’ capaci y o p ocess such la ge
olumes, leading o an expanding gap be ween hem [167]. Nume ous p ojec s and
eposi o ies a e eme ging o s o e inc easingly massi e and in ica e da ase s. Fo
ins ance, The Cance Genome A las (TCGA) [18] amassed 2.5 pe aby es o di e se
biological da a, including mRNA, miRNA, and p o ein exp ession da a, along wi h
his ology slides and gene ic a ia ion da a. The ENCODE p ojec [168] ocuses on
anno a ing unc ional sequences in he human genome, expanding o include da a
om o he o ganisms like mice, lies, and wo ms, o alling o e i e e aby es.
The Eu opean Bioin o ma ics Ins i u e (EBI) [169] s o es mo e han 390 pe a-
by es o aw biological da a, making i one o he la ges eposi o ies globally. As
Biclus e ing Pipeline in Big Da a
Bioin o ma ics wi h HPC In eg a ion
Da a Sou cesGenomic Da a,
Gene Exp ession Ma ices
(HDFS/Cloud S o age)
P ep ocessingNo maliza ion,
Fea u e Selec ion(Spa k + GPU) Biclus e ing Algo i hmsDis ibu ed
FLOCK, Plaid(MPI + CUDA)
Visualiza ionHea maps, D3.js,
Tableau
Applica ionsD ug Disco e y,
Mul i-Omics
Big Da a
HPC
DATA SOURCES DATA
PREPROCESING
BICLUSTERING
VISUALIZATION
Valida ion & E alua ionGO
En ichmen , Scalabili y Me ics
VALIDATION
APPLICATIONS
Fig. 3 Bioin o ma ics Big Da a biclus e ing pipeline wi h HPC in eg a ion (MPI/GPU), designed o
scalabili y in applica ions such as d ug disco e y and mul i-omics in eg a ion

Biclus e ing inbioin o ma ics using big da a andHigh… Page 33 o 52 1123
hese eposi o ies g ow in olume, complexi y, and di e si y, ex ac ing meaning ul
insigh s becomes inc easingly challenging.
The c ea ion and p ep ocessing o da ase s om NGS pla o ms can impac da a
p ecision and quali y due o ac o s like low-quali y eads, duplica e eads, o inse -
ions/dele ions [170]. Reads ep esen he sequenced base pai s (bp) om DNA ag-
men s. Da a ans o ma ion, such as om FASTQ o FASTA, is c ucial o e i ying
da a quali y and elimina ing noise [171]. Howe e , he p oduc ion and p ep ocessing
o la ge da ase s a e ime-consuming p ocesses. To add ess hese challenges, he sci-
en i ic communi y is de eloping Big Da a and HPC ools using dis ibu ed memo y
sys ems [172–174].
The con inuous gene a ion o biological and biomedical da a p esen s challenges
in s o age and managemen . Cloud compu ing has eme ged as an e ec i e solu ion,
as e idenced by a ious s udies [175, 176]. Despi e i s bene i s, e o s a e unde -
way o de elop da a comp ession echniques o educe cloud compu ing cos s [177].
La ency is ano he conce n, as da a e ie al om he cloud o scien i ic da a analy-
sis can be ime-consuming. Shi ing da a p ocessing o he cloud can add ess his
issue, educing la ency and cos s and accele a ing esul gene a ion. Amazon AWS,
o example, o e s he Amazon AWS Genomics se ice, equipped wi h ools o
p ocess la ge olumes o genomic da a e icien ly [178, 179]. Cloud-based s o age
equi es conduc ing da a analysis compu a ions in he cloud o mi iga e la ency in
esul gene a ion. Scalable ools like SeqPig [180], BigBWA [181], GMQL [182],
SeQuiLa-co [183], and SeQual [184] ensu e da a in eg i y du ing quali y con ol
and p ep ocessing, especially when dealing wi h ex ensi e aw sequencing da a.
7.2 Algo i hmic challenges
T adi ional biclus e ing me hods ha e p ima ily ocused on imp o ing esul qual-
i y, bu hey o en s uggle wi h handling la ge da ase s, esul ing in educed use ul-
ness. Challenges in p oducing biclus e s om ex ensi e da ase s, in luenced by a i-
ous ac o s such as applica ion domains, ypes, s uc u es, o dimensions, con ibu e
o his issue [6, 146]. Addi ionally, he cha ac e is ics o la ge biological da ase s
inc ease he likelihood o gene a ing la ge and mo e nume ous biclus e s, equi ing
he use o Big Da a and HPC ools o expedi ed biclus e gene a ion.
To choose he app op ia e Big Da a o HPC applica ion o designing a biclus-
e ing me hodology, unde s anding he p oblem’s na u e, pa allelisable asks, and
da ase cha ac e is ics is c ucial. Fo asks equi ing high p ocessing capabili y wi h
la ge - han-usual bu no massi e da ase s, pa allel o dis ibu ed adi ional models
a e ecommended. Da ase size is pi o al, in luencing memo y managemen , da a
ans e , and communica ion la ency, pa icula ly in MPI-based me hods when using
dis ibu ed memo y o capaci y. This issue does no a ec mul i- h eaded me hods
(e.g. OpenMP o POSIX Th eads), which ely on a single compu e ’s ha dwa e
bu p eclude building CPU clus e s o enhanced pe o mance. To op imise mul i-
h eaded algo i hm pe o mance, i is ecommended o limi pa allel ask complexi y
and comp ess da ase s o con ol p ocessing and s o age cos s.
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1123 Page 34 o 52
Pla o ms like Apache Hadoop o Apache Spa k a e ecommended o handling
la ge da ase s equi ing inc eased p ocessing cos s and compu a ional esou ces,
acili a ing he de elopmen o biclus e ing solu ions o such da ase s. Howe e ,
he e exis s a gap be ween he numbe o biclus e ing algo i hms designed o hese
pla o ms and hose o o he applica ions aimed a imp o ing compu a ional pe o -
mance. Adap ing biclus e ing algo i hms o hese pla o ms o cons uc biclus e s
ia da ase pa i ioning poses c i ical challenges such as he numbe o MapReduce
jobs, in elligen da ase pa i ioning s a egies, educed I/O ope a ions, and asyn-
ch onous communica ion be ween map and educe unc ions. Fu he mo e, hese
biclus e ing echniques o en exhibi poo pe o mance on small da ase s due o pla -
o m ini ialisa ion ime, signi ican ly educing algo i hm e icacy. Some compa a-
i e s udies ha e shown ha biclus e ing algo i hms de eloped using Apache Spa k
o e supe io compu a ional pe o mance compa ed o Apache Hadoop, a ibu ed
o educed I/O ope a ion cos s and as e access speeds.
Recen ly, GPU de ices and he CUDA pla o m ha e become popula o accel-
e a ing biclus e ing algo i hms, demons a ing op imal compu a ional pe o mance.
Howe e , no all GPU-based biclus e ing algo i hms can handle huge da ase s e ec-
i ely, which poses a challenge in de eloping algo i hms o such olumes. Maxim-
ising GPU powe u ilisa ion h ough esou ce planning is c ucial, alongside mini-
mising da a ans e s be ween CPU and GPU o mi iga e bandwid h limi a ions.
While sha ed memo y o e s speed, i s use may lead o memo y o e lows in p o-
cessing massi e da a olumes, equi ing ca e ul selec ion o i s usage. Mul i-GPU
a chi ec u es can enhance pe o mance, bu wo kload dis ibu ion and synch onisa-
ion among GPU de ices a e essen ial. This dis ibu ion ypically employs pa allel
o dis ibu ed me hods like OpenMP, POSIX Th eads, o MPI, while asynch onous
communica ion ac oss GPU de ices has been shown o imp o e compu a ional pe -
o mance by omi ing synch onisa ion.
7.3 Challenges in alida ion
Biclus e ing algo i hms o en gene a e a as numbe o esul s o e la ge da ase s,
which equi e subsequen alida ion [17, 29]. Typically, alida ion in ol es s a is i-
cal o biological knowledge-based me hods, o en sou ced om public da abases, o
asce ain he biological ele ance o he gene a ed ou comes [127]. Recen ad ance-
men s in biclus e alida ion me hods and ools ha e p ima ily ocused on imp o -
ing esul accu acy and de eloping use - iendly in e aces. Howe e , he e is a
no able absence o me hodologies and ools capable o acili a ing e icien compa -
a i e analysis o biclus e alida ion app oaches. Fo ins ance, EBIC, a mul i-GPU
e olu iona y biclus e ing me hod, limi ed i s ou comes o 100 and alida ed hem
using he sequen ial so wa e RGOS a s [160], which ope a es solely wi h gene lis s.
The e is a p essing need o de elop alida ion echniques o la ge biclus e
da ase s. These echniques would enable esea che s o alida e biclus e s de i ed
Biclus e ing inbioin o ma ics using big da a andHigh… Page 35 o 52 1123
om da ase s managed by scien i ic ini ia i es o gene a ed om nex -gene a ion
sequencing. Addi ionally, i has been demons a ed ha i is no possible o d aw
in e es ing biological knowledge om he huge numbe o biclus e s gene a ed by
Big Da a-adap ed biclus e ing algo i hms.
7.4 Visualisa ion andin e p e abili y issues
A e biclus e ing esul s a e alida ed, esea che s mus ocus on isualising and
in e p e ing hem in biological and biomedical con ex s o de i e eliable conclu-
sions. Se e al isualisa ion echniques aid in in e p e ing biclus e ing algo i hm
ou comes. BicO e lappe [185] add esses he challenge o isualising biclus e
o e lap by using in e sec ing hulls, enabling he ex ac ion o biological insigh s
h ough in e ac i e isualisa ion. BiVisu [186] employs Pa allel Coo dina e (PC)
plo s and objec i e me ics like MSR [14] and A e age Co ela ion Value (ACV)
[187] o assess biclus e homogenei y. BiDo s [188] explo es new isual and in e ac-
i e me hods o in es iga ing weigh ed biclus e s ac oss domains. O he conside a-
ions in biclus e isualisa ion include he limi a ions o hea maps and PC plo s in
demons a ing biological ele ance [189]. The au ho s ad oca e o no el isualisa-
ion echniques and p opose s anda ds o biclus e ing algo i hm ou pu s o acili a e
isualisa ion ool in eg a ion. Addi ionally, ne wo k-based isualisa ion app oaches
simpli y he in e p e a ion o biclus e isualisa ion ne wo ks [159, 190].
These isualisa ion ools can help assess he cohe ence o biclus e s o med by
biclus e ing echniques, indica ing hei po en ial signi icance. Howe e , i is s ill
possible ha some biclus e s hold domain- ele an knowledge [146]. Ex e nal
sou ces o domain expe e alua ion may be needed o con i m he au hen ici y o
hese biclus e s. Despi e e o s o s anda dise biclus e ing algo i hm ou pu s and
u ilise isualisa ion and en ichmen analysis ools o be e biological in e p e a-
ion, he shee olume o biclus e s gene a ed in a Big Da a se ing can lead o a
loss o unc ionali y. Consequen ly, he e is a need o isualisa ion ools ailo ed o
enhance biological in e p e a ion and manage la ge biclus e da ase s.
8 Conclusions
The g owing olume o biological and biomedical da a necessi a es he adap a ion o
adi ional biclus e ing me hods o e ec i ely manage la ge-scale da ase s. To mee
his challenge, ecen ad ancemen s ha e ocused on employing High Pe o mance
Compu ing (HPC) and Big Da a amewo ks—such as Apache Spa k, GPU accel-
e a ion, and dis ibu ed/pa allel a chi ec u es— o imp o e compu a ional e iciency.
Al hough hese me hodologies o e subs an ial bene i s, hey also in oduce chal-
lenges ela ed o communica ion o e head, memo y managemen , and ini ialisa ion
A.López-Fe nández e al.
1123 Page 36 o 52
la ency, pa icula ly in MPI-based and MapReduce amewo ks. GPU-based solu-
ions, while powe ul, equi e ca e ul esou ce alloca ion, CUDA g id op imisa-
ion, and e icien da a ans e be ween RAM and GPU memo y o achie e peak
pe o mance.
Beyond compu a ional conside a ions, alida ion is essen ial o con i ming
he eliabili y and biological signi icance o biclus e ing ou comes. Con empo-
a y alida ion s a egies—based on s a is ical consis ency o gene en ichmen
analysis—s uggle o scale wi h he as numbe o biclus e s p oduced by mod-
e n algo i hms. Consequen ly, many ools limi hei assessmen s o a subse o
esul s, highligh ing he need o mo e scalable and au oma ed alida ion ech-
niques ha le e age HPC in as uc u e.
This s udy has iden i ied mul iple a enues o imp o emen . Fu u e esea ch
should ocus on de eloping biclus e ing algo i hms ha a e inhe en ly scal-
able and op imised o dis ibu ed and he e ogeneous compu ing en i onmen s.
Imp o ing he in e p e abili y o biclus e ing esul s is also essen ial o acili a e
adop ion by biomedical esea che s, which may in ol e he use o in e p e able
models o isualisa ion ools ha cla i y he biological ele ance o he de ec ed
pa e ns. S anda dised, open benchma king amewo ks ha ely on eal-wo ld
da ase s a e also needed o suppo ep oducible and compa a i e assessmen s.
One p omising app oach in ol es in eg a ing p io biological knowledge—
such as unc ional anno a ions o egula o y ne wo ks—in o he biclus e ing
p ocess o guide he disco e y o biologically meaning ul s uc u es. The ield
can also bene i om he inco po a ion o ad anced machine lea ning me hods,
including deep lea ning, g aph-based models, and eme ging o ms o a i icial
in elligence such as ein o cemen lea ning and ans o me s. These echnolo-
gies a e expec ed o play a cen al ole in he nex gene a ion o biclus e ing
me hods, enabling mo e adap i e, con ex -awa e, and au oma ed disco e y o
complex pa e ns in omics da a.
Expanding he applica ion o biclus e ing o mul i-omics in eg a ion and ime
se ies analysis could p o ide mo e comp ehensi e insigh s in o dynamic bio-
logical sys ems. Fu he mo e, u u e algo i hms should enhance obus ness o
noise h ough he use o obus s a is ical echniques and p ep ocessing s a -
egies. S eng hening collabo a ion wi h expe imen al biologis s is also key o
alida ing compu a ional indings in i o o in i o, he eby suppo ing hei
ansla ional ele ance.
Looking ahead, an icipa ed mains eam ends include he in eg a ion o
biclus e ing in o au oma ed machine lea ning (Au oML) pipelines, enabling
non-expe use s o conduc explo a o y analyses in biomedical con ex s. Addi-
ionally, he deploymen o biclus e ing wo k lows wi hin cloud-based HPC
en i onmen s will enhance scalabili y and accessibili y. Ano he impo an
di ec ion is he explo a ion o ede a ed lea ning amewo ks o enable secu e
Biclus e ing inbioin o ma ics using big da a andHigh… Page 37 o 52 1123
and p i acy-p ese ing biclus e ing ac oss dis ibu ed biomedical da ase s—a
c ucial conside a ion in mul i-ins i u ional and clinical se ings.
By add essing hese limi a ions and oppo uni ies, nex -gene a ion biclus e -
ing echniques can e ol e in o powe ul, in e p e able, and scalable ools o
analysing high-dimensional biological da a. The ising adop ion o deep lea n-
ing and AI-d i en me hodologies in bioin o ma ics ep esen s a p omising di ec-
ion. In es iga ing how such models can be e ec i ely adap ed wi hin HPC and
Big Da a ecosys ems—pa icula ly in alignmen wi h he analy ical s a egies
discussed in his pape —cons i u es a signi ican a ea o u u e esea ch.
Appendix A: De ailed ables
See Tables2, 3, 4, 5, 6, 7, 8 and 9
All he ables ha a e p esen ed below a e e e enced in he main ex .
Table 2 A summa y o he mos signi ican biclus e ing e iews
Yea Au ho s Applica ion Me hods Valida ion
2004 Madei a e al. [19] Gene exp ession X
2005 Tanay e al. [23] Gene exp ession X
2007 San ama ía e al. [24] Gene exp ession X
2008 Busygin e al. [20] Biomedicine and ex mining X
2010 Ras ega e al. [191] Gene exp ession X
2010 Ve ma e al. [192] Gene exp ession X
2013 E en e al. [25] Gene exp ession X
2013 O zechowski. [128] Gene exp ession X
2014 Oghabian e al. [193] Gene exp ession X
2014 Ho a e al. [194] Gene exp ession X
2015 Pon es e al. [195] Gene exp ession X
2015 Pon es e al. [21] Gene exp ession X
2015 Mouni . [196] Gene exp ession X
2016 Biswal e al. [197] Gene exp ession X
2016 Ani ha e al. [198] Gene exp ession X
2017 Padilha e al. [26] Gene exp ession X X
2018 Biswal e al. [199] Gene exp ession X
2018 Aouabed e al. [200] Biomedical X
2019 Xie e al. [6] Biological and biomedical X
2021 Nicholls e al. [27] Gene exp ession X
2021 Sozdinle , [189] Gene exp ession X
2022 No onha e al. [146] Da a mining and gene exp ession X
2022 José-Ga cía e al. [22] Gene exp ession X
2024 Cas anho e al. [28] Biological and biomedical X X

A.López-Fe nández e al.
1123 Page 38 o 52
Table 3 Reposi o ies whe e he sou ce codes o he HPC biclus e ing algo i hms a e s o ed
Yea Me hod Sou ce code URL
2007 RoBA [74] –
2008 DisCo [107] –
2008 P-Biclus e [77] –
2009 A.Nisa e al [80] –
2012 FLOC [113] –
2013 GBC [17] –
2014 GBC [117] –
2014 MCC [106] –
2014 BiTM-MR [100]h ps:// gi hub. com/ Tugdu alSa azin/ spa k- clus e ing
2014 Cloudnm [101]h p:// admis. udan. edu. cn/ p oje c s/ Cloud NMF. h ml
2015 NMF [118]h ps:// gi hub. com/ bioin o- cnb/ bionm - gpu
2015 Bha naga e al. [108] –
2015 MFCM [83] –
2016 MR-GABiT [104] –
2017 CCS [120]h ps:// gi hub. com/ abha a3/ Condi ion- depen den -
Co e la ion- Subg oups- CCS
2017 PBD-SPEA2 [96] -
2018 EBIC [121]h ps:// gi hub. com/ Epis asisL ab/ ebic
2018 Runibic [85]h ps:// www. bioco nduc o . o g/ packa ges/ elea se/
bioc/ h ml/ unib ic. h ml
2018 Pa BiBi [88]h ps:// sou c e o ge. ne / p oje c s/ pa bi bi /
2019 CuBiBi [122]h ps:// sou c e o ge. ne / p oje c s/ cubib i
2019 SP-PLSS [4] –
2021 ScalaPa BiBi [91]h ps:// gi hub. com/ agu ela/ Scala Pa Bi Bi
2021 COBRAC [92]h ps:// gi hub. com/ haidyi/ c xbi clus
2021 gBiBi [90]h ps:// gi hub. com/ au el iol d ez/ gbibi
Biclus e ing inbioin o ma ics using big da a andHigh… Page 39 o 52 1123
Table 4 Main ea u es o biclus e ing algo i hms accele a ed by adi ional pa allel and dis ibu ed mod-
els
Da a compa ibili y
Yea Me hod Da ase s Compa a i e Noise Da a Bina y Disc e e Con inuous
2007 RoBA [74] 2 0 No Mic oa ays X
2008 P-Biclus e
[77]
1 1 No Mic oa ays X X
2009 Nisa e al [80] 0 0 Yes Mic oa ays X
2015 MFCM [83] 1 2 No Mic oa ays X
2017 PBD-SPEA2
[96]
2 1 Yes Mic oa ays X
2018 Runibic [85] 1 1 Yes RNA-Seq X X
2018 Pa BiBi [88] 2 1 No Mic oa ays X
2021 ScalaPa BiBi
[91]
0 2 No Mic oa ays X
2021 COBRAC [92] 1 0 Yes Mic oa ays X
2022 ARBic [94] 2 1 No Mic oa ays X
2023 EnsemBic [95] 1 1 Yes Mic oa ays &
RNA-Seq
X
Table 5 Compu a ional ea u es o biclus e ing algo i hms based on adi ional pa allel and dis ibu ed
models
Yea Me hod Language Accele a ion Op imisa ion Com-
munica-
ions
2007 RoBA [74] MATLAB MPI No Yes
2008 P-Biclus e [77] ANSI C MPI No No
2009 A.Nisa e al [80] C/C++ MPI Yes Yes
2015 MFCM [83] MATLAB MPI No No
2017 PBD-SPEA2 [96] - - - -
2018 Runibic [85] C/C++ OpenMP No No
2018 Pa BiBi [88] C/C++ MPI/POSIX No Yes
2021 ScalaPa BiBi [91] C/C++ MPI/POSIX Yes Yes
2021 COBRAC [92] C/C++ OpenMP Yes No
2022 ARBic [94] C/C++ OpenMP No No
2023 EnsemBic [95] R - - -
A.López-Fe nández e al.
1123 Page 40 o 52
Table 6 Main ea u es o biclus e ing algo i hms suppo ed by MapReduce pla o ms
Da a compa ibili y
Yea Me hod Pla o m Da ase s Compa a i e Noise Da a Bina y Disc e e Con inuous
2008 DisCo [107] Hadoop 1 0 No - X
2014 MCC [106] - 1 1 No Mic oa ays X
2014 BiTM-MR [100] Spa k 0 0 No - X
2014 Cloudnm [101] Hadoop 2 0 No PPI X
2015 Bha naga e al. [108] Hadoop 1 2 No - X
2016 MR-GABiT [104] MATLAB 1 0 No Mic oa ays X X
2019 SP-PLSS [4] Spa k 2 2 No – X X
Biclus e ing inbioin o ma ics using big da a andHigh… Page 41 o 52 1123
Table 7 Compu a ional ea u es o biclus e ing algo i hms based on MapReduce pla o ms
Yea Me hod MR Jobs Pa i ioning I/O In-memo y Asyn-
ch o-
nous
Speedup Scalabili y
2008 DisCo [107] 1 0 0 0 0 1 3
2014 MCC [106] 1 0 0 0 0 0 1
2014 BiTM-MR [100] 1 0 1 1 1 1 3
2014 Cloudnm [101] 1 0 0 0 0 0 1
2015 Bha naga e al. [108] 1 0 0 0 0 0 3
2016 MR-GABiT [104] 0 1 0 0 0 1 1
2019 SP-PLSS [4] 1 1 1 1 0 1 3
Table 8 Main ea u es o biclus e ing algo i hms suppo ed by GPGPU pla o ms
Da a compa ibili y
Yea Me hod Da ase s Compa a i e Noise Da a Bina y Disc e e Con inuous
2012 FLOC [113] 0 1 No Mic oa ays X X
2013 GBC [17] 1 2 No Mic oa ays X
2014 GBC [117] 1 2 No Mic oa ays X
2015 NMF [118] 1 1 No Mic oa ays X
2017 CCS [120] 2 1 No Mic oa ays X X
2018 EBIC [121] 2 2 Yes Mic oa ays X X X
2019 CuBiBi [122] 0 1 No Mic oa ays X
2021 gBiBi [90] 2 2 No Mic oa ays &
RNA-Seq
X
Table 9 Compu a ional ea u es o biclus e ing algo i hms based on GPGPU pla o ms
Yea Me hod mul i-GPU Sha ed
mem.
Coalescing Alloca ion T ans e Occupancy Sync.
2012 FLOC [113]No No No 0 Global Low No
2013 GBC [17]No Yes Yes 1 Global Low No
2014 GBC [117]No Yes Yes 1 Global High Yes
2015 NMF [118] Yes (MPI) Yes Yes 1 Global High Yes
2017 CCS [120]No Yes No 1 Global Low No
2018 EBIC [121] Yes
(OpenMP)
Yes Yes 1 Global High Yes
2019 CuBiBi
[122]
Yes
(POSIX)
Yes Yes 1 Global Low No
2021 gBiBi [90] Yes
(POSIX)
No Yes 1 Global High No
A.López-Fe nández e al.
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Publishe ’s No e Sp inge Na u e emains neu al wi h ega d o ju isdic ional claims in published maps
and ins i u ional a ilia ions.
A.López-Fe nández e al.
1123 Page 52 o 52
Au ho s and A ilia ions
Au elioLópez‑Fe nández4· F anciscoA.Gomez‑Vela1·
DomingoS.Rod iguez‑Baena1· Fe nandoM.Delgado‑Cha es2·
Jo geGonzalez‑Dominguez3
* Au elio López-Fe nández
[email p o ec ed]
F ancisco A. Gomez-Vela
[email p o ec ed]
Domingo S. Rod iguez-Baena
[email p o ec ed]
Fe nando M. Delgado-Cha es
e nando.miguel.delgado-cha es@uni-hambu g.de
Jo ge Gonzalez-Dominguez
jo g[email p o ec ed]
1 In elligen Da a Analysis G oup (DATAi), Uni e sidad Pablo de Ola ide, C a. U e a, km. 1,
ES-41013Se ille, Spain
2 Ins i u e o Compu a ional Sys ems Biology, Uni e si y o Hambu g, No kes asse 9,
22607Hambu g, Ge many
3 Compu e A chi ec u e G oup, Uni e sidade da Co uña, Campus de El iña, 15071ACo uña,
Spain
4 Dp o. Lenguajes y Sis emas In o má icos, Uni e sidad de Se illa, Se ille, Spain