Machine learning to differentiate diseased cardiomyocytes from healthy

Con en s lis s a ailable a ScienceDi ec In o ma ics in Medicine Unlocked jou nal homepage: www.else ie .com/loca e/imu Machine lea ning o diffe en ia e diseased ca diomyocy es om heal hy con ol cells Ma i Juhola a,∗ , Hen y Jou sijoki a , Ki si Pen inen b , Ka iina Aal o-Se älä b,c a Facul y o In o ma ion Technology and Communica ion Sciences, Tampe e Uni e si y, Finland b Facul y o Medicine and Heal h Technology, Tampe e Uni e si y, Finland c Hea Cen e , Tampe e Uni e si y Hospi al, 33520, Tampe e, Finland ARTICLE INFO Keywo ds: Calcium ansien p ofiles Gene ic ca diac diseases Machine lea ning Diffe en ia ion o he diseased om con ols ABSTRACT Human induced plu ipo en s em cell-de i ed ca diomyocy es (iPSC-CMs) ha e been shown o be use ul o imp o e echniques ha a e de eloped o he s udy o ca diac disease. Abno mali ies in Ca 2+ ansien s a e commonly p esen in iPSC-CMs de i ed om indi iduals wi h a ca diac disease. We p e iously obse ed ha Ca 2+ ansien signals o heal hy CMs can be dis inguished om ansien s o CMs de i ed om indi iduals ha ing diffe en gene ic ca diac diseases. Machine lea ning was used o dis inguish diffe en diseases om each o he as well as om con ols. We wan ed u he o in es iga e whe he we a e able o sepa a e iPSC-CM Ca 2+ signals o any gene ic ca diac disease as one g oup om hose o heal hy indi iduals by u ilizing machine lea ning me hods. A o al numbe o 593 CM ansien signals om heal hy indi iduals and om pa ien s we e analyzed. We ob ained a bes classifica ion accu acy o 87% be ween he disease g oup and con ols. This finding p o ides e idence ha machine lea ning me hods a e efficien o iden i ying iPSC-CMs de i ed om indi iduals wi h a disease pheno ype, and ha iPSC-CMs may be use ul o iden i y indi iduals a isk o a ca diac e en . 1. In oduc ion Gene ic ca diac diseases p esen a wide ange o symp oms, anging om comple ely asymp oma ic o se e e a hy hmias, and e en sudden ca diac dea h [1,2]. Addi ionally, mos i no all o hese diseases ha e an inc eased isk o a hy hmia, in addi ion o s uc u al o o he ca diac abno mali y, e.g., in a ious ca diomyopa hies. I he mu a ion causing disease is known in he amily, i is easy o ocus on he mu- a ion ca ie s o ollow-up and p ima y p e en ion o po en ial a - hy hmias. Howe e , his is o en p oblema ic when he disease is p esen ed by sudden dea h in he amily, bu no mu a ions a e ound. In such si ua ions, induced plu ipo en s em cell (iPSC) -de i ed ca dio- myocy es may p o ide a use ul al e na i e o p edic a hy hmic isk, and o iden i y hose amily membe s wi h inc eased isk o clinical symp oms including a hy hmias. On a cellula le el, ca diac unc ionali y can be s udied wi h he help o CMs diffe en ia ed om human plu ipo en s em cells [3,4]. The induced plu ipo en s em cell (iPSC) echnology offe s a way o ep o- g am diffe en ia ed cells back o he plu ipo en s a e –and, he e o e, i is a use ul ool o s udying he pa hophysiology o a ious diso de s and d ug esponses in human cells [5]. Addi ionally, cellula diffe - en ia ion and ma u a ion can be s udied wi h hese cells [6]. Thus a , iPSC-CMs ha e success ully been used o model gene ic ca diac diseases such as ca echolamine gic polymo phic en icula achyca dia (CPVT) [3,4,7–12], long QT synd ome (LQT) [13–16] and hype ophic ca diomyopa hy (HCM) [17–19]. iPSC-de i ed CMs ha e e ealed conside able abno mali ies and di e si y in in acellula Ca 2+ cycling ea u es compa ed o heal hy con ol CMs. Ca 2+ cycling plays an impo an ole in ca diac unc ionali y by linking elec ical ac i a- ion and con ac ion, and he cha ac e iza ion o Ca 2+ cycling is i al in o de o acili a e in es iga ions o ca diac diso de s and dys unc ions, as well as o s udy disease managemen wi h diffe en compounds. He e o o e, machine lea ning has a ely been applied o he da a associa ed wi h induced plu ipo en s em cell-de i ed ca diomyocy es. Machine lea ning has howe e been applied o he mechanis ic ac ion o ca dioac i e d ugs [20]. We demons a ed in p e ious a icles ha he sepa a ion o calcium ansien signals o abno mally and no mally g own ca diomyocy es can be accu a ely done wi h machine lea ning [21,22]. In hese pape s, abno mali y is defined as de o med Ca 2+ peak o ms a ying in ampli udes (sizes) and du a ions o Ca 2+ ansien s and no mali y as ha monious ansien s o app oxima ely he same size and o m h oughou en i e calcium ansien signals. To he bes o ou knowledge, ou ecen s udy [23] was he fi s one in which diffe en gene ic ca diac diseases we e sepa a ed acco ding o hei calcium h ps://doi.o g/10.1016/j.imu.2019.01.006 Recei ed 17 Decembe 2018; Recei ed in e ised o m 16 Janua y 2019; Accep ed 31 Janua y 2019 ∗ Co esponding au ho . E-mail add ess: Ma i.Juhola@ uni.fi(M. Juhola). In o ma ics in Medicine Unlocked 14 (2019) 15–22 A ailable online 02 Feb ua y 2019 2352-9148/ © 2019 Published by Else ie L d. This is an open access a icle unde he CC BY-NC-ND license (h p://c ea i ecommons.o g/licenses/BY-NC-ND/4.0/). T ansien signals by using classifica ion pe o med wi h machine lea ning me hods. In he p esen esea ch, isually no mal and abno mal Ca 2+ an- sien signals and peak a iables o h ee gene ic ca diac diseases and heal hy con ol CMs we e used. Disease-specific CMs we e gene a ed om pa ien s suffe ing om CPVT, an exe cise-induced malignan a - hy hmogenic diso de [3,8], LQT ype 1, an elec ic diso de o he hea ha p edisposes pa ien s o a hy hmias, and HCM, a diso de ha affec s he s uc u e o hea muscle issue leading o a hy hmias and p og essi e hea ailu e [19]. All o he ansien s o diseased cells we e pooled and compa ed o all o he ansien s ob ained om con ol cells, and diffe en machine lea ning algo i hms we e designed and used o analyze his echnology o au oma ically dis inguish he g oups. 2. Ma e ials The cu en s udy was app o ed by he E hics Commi ee o Pi kanmaa Hospi al Dis ic in es ablishing, cul u ing and diffe - en ia ing human iPSC lines (R08070). The pa ien -specific iPSC lines we e es ablished and cha ac e ized as desc ibed ea lie , as well as he CM diffe en ia ion and dissocia ion o bea ing a eas [22]. The s udied cell lines included six CPVT lines gene a ed om CPVT pa ien s ca - ying ca diac yanodine ecep o (RyR2) mu a ions, ou HCM cell lines gene a ed om HCM pa ien s ca ying ei he α- opomyosin (TPM1) o myosin-binding p o ein C (MYBPC3) mu a ions, wo LQT ype 1 cell lines gene a ed om pa ien s ca ying po assium ol age-ga ed channel sub amily Q membe 1 (KCNQ1) mu a ions, and one cell line gene a ed om a heal hy con ol indi idual. Thus, he e we e 13 subjec s al o- ge he . Ca 2+ imaging was conduc ed in spon aneously bea ing, 4 μM Fu a-2 AM (In i ogen, Molecula P obes) o 4 μM Fluo-4 AM (Li e Technologies L d) loaded dissocia ed CMs as desc ibed ea lie [3]. Du ing he measu emen s, CMs we e pe used wi h 37 °C HEPES based pe usa e consis ing o (in mM) 137 NaCl, 5 KCl, 0.44 KH2PO4, 20 HEPES, 4.2 NaHCO3, 5 D-glucose, 2 CaCl2, 1.2 MgCl2, and 1 Na-py - u a e ( he pH was adjus ed o 7.4 wi h NaOH). Ca 2+ measu emen s we e conduc ed on an in e ed IX70 mic oscope wi h a UApo/340 x20 ai objec i e (bo h Olympus Co po a ion, Hambu g, Ge many) o wi h Axio Obse e .A1 mic oscope wi h Objec i e Flua 20x/0.75 M27 (bo h Ca l Zeiss Mic oscopy GmbH, Gö ingen, Ge many). Images we e aken wi h an ANDOR iXon 885 CCD came a (Ando Technology, Bel as , No he n I eland) and synch onized wi h a Polych ome V ligh sou ce by a eal ime DSP con ol uni o wi h Lambda DG-4 Plus (Su e In- s umen , Cali o nia, USA) wa eleng h swi che and TILL isION, Li e Acquisi ion (TILL Pho onics, Munich, Ge many) o ZEN 2 blue edi ion so wa e (Ca l Zeiss Mic oscopy GmbH, Gö ingen, Ge many) so wa e. Fo Ca 2+ analysis, egions o in e es we e selec ed o spon aneously bea ing cells and backg ound noise was sub ac ed be o e u he p o- cessing. Each Ca 2+ signal co esponded o a eco ding om one cell. 3. Da a compu ed om Ca 2+ ansien signals Human induced plu ipo en s em cell-de i ed CMs we e he da a sou ce om which cycling Ca 2+ ansien signals we e ob ained. Da a used in he compu a ion we e based on he peaks o Ca 2+ ansien signals. Cycling peaks we e ecognized, and da a a iables o ea u es we e ex ac ed om e e y peak. P e iously Ca 2+ ansien signals we e ca ego ized using ou ecogni ion algo i hm [22], which classified hem in o ei he no mal ype o abno mal signal ype on he basis o no mal o abno mal peaks o he signals, and whe e we obse ed ha i was possible o sepa a e no mal om abno mal signals up o he accu acy o app oxima ely 90% when compa ed o a human expe 's classifica ion decisions. Fig. 1 p esen s 10 s segmen s o ou signals as examples. The signals we e sho , hei du a ions being om 7.7 s o 46.5 s and 19.0 s on a e age. An en i e signal was de e mined o be abno mal i e en a single peak was obse ed as abno mal. In he cu en esea ch, we did no diffe no mal and abno mal signals om each o he , bu ad anced o sepa a e Ca 2+ ansien signals o diseased induced plu ipo en s em cell-de i ed CMs om hose o con ol subjec s. I was in e es ing o s udy whe he disease ansien signals can be diffe en ia ed om hose o con ols, al hough bo h classes con ained bo h no mal and abno mal signals. The numbe o he abno mal con ol signals was only 12.6% o all con ol signals. The disease ansien signals o igina ed om he g oup o he h ee abo e-men ioned diseases: LQT1, HCM and CPVT. These we e used join ly as he disease class. The o he class was o med by he signals o he con ols (wild ype, WT). The da a used comp ised 394 disease ansien signals and 199 con ol ansien signals. These con ained, espec i ely, 179 no mal and 215 abno mal signals, and 174 no mal and 25 abno mal signals. I is no iceable ha he e we e only ela i ely ew abno mal signals in con ols, since hese we e a mo e in equen compa ed o hose o disease CMs. Sampling o he ansien signals con ained h ee diffe en e- quencies, because he da a we e eco ded a diffe en imes and he sampling equency was inc eased in he mean ime. The app oxima e sampling equencies we e 8 Hz, 11 Hz and 23 Hz. In his o de , 35%, 26% and 39% o he diseased signals we e eco ded, and, co espond- ingly, 5%, 15% and 80% o he con ol signals. In o de o de ec indi idual peaks o a ansien signal, alues o i s fi s de i a i e we e compu ed in sho sequen ial segmen s, whe e slopes o linea eg ession compu ed wi h sequen ial signal segmen s o a ew samples we e used o es ima e fi s de i a i e alues (Fig. 2). To de e mine he beginning o a peak, i s fi s de i a i e alues had o emain smalle han a small posi i e h eshold alue de e mined ex- pe imen ally du ing a ew sequen ial slope alues. The ea e , slope alues became g ea e while p oceeding o wa d along a ypically s eep le side o a peak p oducing la ge posi i e fi s de i a i e slope alues. Nex a peak maximum o op was ound when slope alues again de- c eased less han he posi i e h eshold alue. A e he maximum, fi s de i a i e slope alues changed nega i e along he dec easing igh side o a peak (Fig. 2). Ul ima ely, he end o a peak was encoun e ed when he fi s de i a i e slope alues again inc eased close o ze o. The de ailed p ocedu e o he peak de ec ion was in oduced in ou p e- ious esea ch [22]. Howe e , oscilla ions o e y small ampli udes we e no accep ed as alid peaks in a signal, as ollows. A e he e- mo al o a possible linea end in a signal, he ampli ude o la ge peaks in a signal was es ima ed as a diffe ence om he a e age o he highes sample (ampli ude) alues (15% o all) o he lowes sample alues in he cu en signal. Such peak candida es ha had he ampli ude o he le o igh side o a peak less han app oxima ely 8% om he abo e ampli ude es ima e o he la ge peaks we e no accep ed as peaks, bu we e suspec ed as being p obable noise [22]. The numbe s o he peaks ex ac ed om he signals a ied om 1 o 61, and we e only 12.8 on a e age. A e he ecogni ion o he peaks om he ansien signals, alues o 12 a iables o e e y peak we e compu ed as ollows. Fi s , he ampli udes o he le and igh side o a peak we e compu ed - see Fig. 2. Second, he du a ions o bo h peak sides we e compu ed om loca ions a o cand om c o e. Thi d, he maximum o he fi s de i- a i e om he le side o a peak and he absolu e minimum o he fi s de i a i e om he igh side we e compu ed. Fou h, he maximum and absolu e minimum o he second de i a i e we e compu ed om he igh side only. The le side was no now applied, because e- quen ly hese we e oo sho (as o he numbe o samples) o second de i a i e alues o ha e been calcula ed. Fi h, he su ace de e mined by a peak cu e and a line om he beginning o he end o a peak was compu ed. Six h, he du a ion ( ime diffe ence) om he maximum a loca ion c o he maximum o he p eceding peak was compu ed, o i he cu en peak was he fi s peak o a signal, he du a ion was cal- cula ed om he signal beginning. Se en h, he du a ion ( ime diffe - ence) om peak beginning a o loca ion bo he fi s de i a i e M. Juhola e al. In o ma ics in Medicine Unlocked 14 (2019) 15–22 16 maximum o he le peak side and he du a ion om loca ion co he peak maximum o loca ion do he fi s de i a i e minimum o he igh peak side we e compu ed. To isualize he da a, in o he wo ds, a iable alues compu ed om 5290 ecognized peaks o he disease ansien signals and 2291 peaks o he con ol ansien signals, a e he no maliza ion o he da a S ochas ic Neighbo Embedding algo i hm wi h he Euclidean dis ance measu e in MATLAB was used o p esen he da a in wo di- mensions. This is depic ed in Fig. 3. When a g ea pa o he cases in wo diffe en classes a e apa om each o he , i is possible ha his p edic s a success ul classifica ion o he classes. The means and s anda d de ia ions o all 12 a iables a e p esen ed in Table 1. Conside able diffe ences o he means o wo classes o e e y a iable a e seen, which may deno e a a o able classifica ion chance be ween he disease and con ol ansien signals. Nex we e alua ed how efficien he 12 a iables a e o sepa a e o classi y he wo classes. We an he elie F algo i hm in MATLAB o ou da a. I unc ions on he basis o applying a nea es neighbo sea ching me hod, in o de o measu e he diffe en ia ion powe o weigh , a iable by a iable. We chose nine k alues o he numbe s o nea es neighbo s, con ol pa ame e o he algo i hm. They we e 3, 5, 7, 9, 11, 15, 21, 25 and 31, whe e odd alues only we e used o p e en possible ies (equal numbe s om he wo opposi e classes) du ing nea es neighbo sea ching. Fo each peak a iable, he median o weigh s gi en by he elie F algo i hm o esul s o nine uns (Fig. 4) was compu ed. The posi i e weigh s mean ha all a iables a e able o se- pa a e he wo classes. Weigh s a e ela i e and hey exp ess anking o a iables o diffe en ia ion o classes. Fo all nine uns, a iables 3, 4, 5 Fig. 1. (a) A 10 s segmen o a no mal CPVT signal in which he peaks ecognized o be no mal a e ma ked wi h g een ba s, and also he beginning and end o e e y peak ma ked. (b) An abno mal CPVT signal in which all peaks we e ecognized as de o med o be abno mal and ma ked wi h s a s. (c) A 10 s segmen o a no mal con ol ansien signal. (d) An abno mal con ol ansien signal, whe e wo abno mal peaks we e ma ked wi h s a s. (Fo in e p e a ion o he e e ences o colou in his figu e legend, he eade is e e ed o he Web e sion o his a icle.) M. Juhola e al. In o ma ics in Medicine Unlocked 14 (2019) 15–22 17 and 11 ob ained he g ea es o bes weigh s and a iables 9, 10 and 12 esul ed in he smalles o poo es ins ances o sepa a e he wo classes. Ins ead, a iables 1, 2, 6, 7 and 8 we e be ween he bes and poo es , and hei anking a ied sligh ly among he nine es uns, when hei Fig. 2. The peak is he fi s one om he signal in Fig. 1(a). The sho ed lines isualize imagina y slopes (upwa d is posi i e, downwa d nega i e and ho izon al ze o slope alue) o he fi s de i a i e cu e o he signal du ing he cu en peak. The peak begins om loca ion a; i s maximum is a cand end a e. Loca ion bis o he maximum o he fi s de- i a i e and d o i s minimum. (Fo in e p e a ion o he e e ences o colou in his figu e legend, he eade is e e ed o he Web e sion o his a icle.) Fig. 3. The isualiza ion o he peak a iable alues o disease and con ol (WT) ansien signals in wo dimensions. Table 1 Means and s anda d de ia ions o 12 a iables o 5290 peaks o he disease ansien signals and 2291 peaks o he con ol ansien signals. Va iable numbe Peak a iable Disease ansien signal peaks Con ol ansien signal peaks 1 Peak le side ampli ude 201.3 ± 134.4 272.5 ± 170.2 2 Peak igh side ampli ude 203.4 ± 135.4 275.4 ± 171.8 3 Le du a ion [s] 0.31 ± 0.181 0.492 ± 0.263 4 Righ du a ion [s] 0.597 ± 0.394 1.039 ± 0.601 5 Maximum o le side fi s de i a i e 1348 ± 985 2131 ± 1276 6 Absolu e minimum o igh side fi s de i a i e 780 ± 496.33 927 ± 635 7 Maximum o igh side second de i a i e 3397 ± 3110 4465 ± 3386 8 Absolu e minimum o igh side second de i a i e 2116 ± 2561 3938 ± 4359 9 Peak a ea 68 ± 76 132 ± 115 10 Du a ion om peak maximum o p eceding one (o beginning) [s] 1.039 ± 0.858 1.944 ± 1.58 11 Du a ion om peak beginning [s] o le side maximum o fi s de i a i e 0.201 ± 0.14 0.312 ± 0.198 12 Du a ion om peak maximum [s] o igh side minimum o fi s de i a i e 0.138 ± 0.075 0.156 ± 0.145 Fig. 4. Medians o weigh s we e calcula ed wi h he elie F algo i hm o he 12 a iables o he cu en da a by using 9 diffe en k alues (numbe o nea es neighbo s). Va iables 3, 4, 5 and 11 a e he mos efficien and a iables 9, 10 and 12 he leas efficien o he diffe en ia ion o he disease ansien signals om hose o con ols. M. Juhola e al. In o ma ics in Medicine Unlocked 14 (2019) 15–22 18 weigh s we e app oxima ely equal as seen in Fig. 4. In any case, on he basis o hese esul s as well hose in Table 1, all 12 a iables we e ound o be use ul o he diffe en ia ion o disease and con ol calcium ansien signals om each o he . 4. Classifica ion me hods used and design o expe imen s We used a wide collec ion o classifica ion algo i hms in ou s udy, anging om adi ional me hods o s a e-o - he-a me hods. Since ou da ase consis s o a ue class label defined by he human expe o each signal, we concen a ed only on supe ised classifica ion me hods and, hence, semi-supe ised and unsupe ised me hods we e ou o he scope o his pape . Me hods used in ou s udy we e mos ly he same as used in ou p e ious s udy [23]. Howe e , in Re . [23] he classifica ion ask was mul i-class by na u e, whe eas in his pape he classifica ion ask is a wo-class p oblem. We do no p esen he de ailed desc ip ion abou he ac ual classifica ion me hods, bu a eade can find ho ough desc ip ions abou he algo i hms om he gi en e e ences. Compa ed o Re . [23], he e we e now wo no el peak a iables: a iables 11 and 12 (Table 1). As a fi s classifica ion algo i hm, we applied he kNea es Neighbo (kNN) sea ching me hod [24–26] ha is one o he ea lies classifica- ion algo i hms and mos used. The pe o mance o he kNN algo i hm depends mainly on h ee ac o s: k alue, dis ance measu e, and dis- ance weigh ing scheme. These ac o s a e da a-dependen and o each da ase , a sui able combina ion mus be sea ched sepa a ely. Fo ou s udy, we selec ed k alues o 1, 5, 7, 11, 13 and 17 o be examined, and he e we ollowed he p inciple used in Re . [23]. We selec ed eigh dis ance measu es o be es ed: Chebyshe , ci yblock (Manha an), co ela ion, cosine, Euclidean, Mahalanobis, s anda dized Euclidean and Spea man. We pe o med classifica ion wi h h ee dis ance weigh ing schemes: no weigh ing (weigh ing equal o 1), ecip ocal, and squa ed ecip ocal wi h espec o dis ance. The second wholeness used was disc iminan analysis based algo- i hms. Disc iminan analysis co e s a ious a ia ions, and om hem we applied linea disc iminan analysis [27,28], quad a ic disc iminan analysis [28,29], and Mahalanobis dis ance based disc iminan analysis [30]. When mo ing o p obabili y based algo i hms, naï e Bayes clas- sifie [24,31,32] canno be dismissed. Naï e Bayes is a classical widely used me hod in many applica ions. I can be used wi h o wi hou ke nel densi y es ima ion [24,31]. In his pape we used he naï e Bayes algo i hm in bo h senses. When ke nel densi y es ima ion was used, we examined Gaussian, box, iangle and Epanechniko ke nels. We also applied he naï e Bayes classifie wi hou ke nel densi y es ima ion when he no mal dis ibu ion assump ion o e he da ase is expec ed. Besides naï e Bayes classifie , kNN and disc iminan analysis based classifica ion me hods, we used mul inomial logis ic eg ession [33,34] which e u ns o logis ic eg ession [31,32] in wo-class asks. Decision ee-based solu ions a e commonly used al e na i es in machine lea ning asks. Thei ad an ages a e easy in e p e a ion and compu a ional efficiency. These issues a e impo an o ake in o ac- coun when conside ing he end-use s o ou applica ion, who a e pe sons no expe s in he machine lea ning a ea. In ou s udy, we in- es iga ed CART [25,35] algo i hm and Random Fo es s [36–38]. Fo Random Fo es s we a ied he numbe o ees om 1 o 100. The Suppo Vec o Machine [39] has gained g ea popula i y since he ea ly 1990s, and has been used in a ious applica ions. Howe e , in ou s udy, we decided o use a a ian o SVM called he leas squa es suppo ec o machine (LSSVM) [40–42] which diffe s om adi ional SVM in such a way ha LSSVM sol es a sys em o linea equa ions ins ead o a quad a ic op imiza ion p oblem. The pe o mance o LSSVM is hea ily dependen on he selec ion o a ke nel unc ion and (hype )pa ame e alues. Hence, i is always necessa y o pe o m a ho ough sea ch o (hype )pa ame e alues in o de o ensu e he bes possible esul . A common pa ame e o all ke nel unc ions in LSSVM is C, also called box cons ain . We selec ed he linea , quad a ic, 3 d deg ee o polynomial ke nel and he RBF ke nel o be examined in ou pape . The pa ame e alue space o he box cons ain and hy- pe pa ame e σ( he wid h o Gaussian unc ion) is {2 −12 ,2 −11 , …,2 16 ,2 17 }. By his means polynomial ke nels we e es ed on 30 pa ame e alues and he RBF ke nel on 900 (C,σ) combina ions. The classifica ion was pe o med based on he lea e-one-signal-ou (LOSO) p ocedu e, which is a modifica ion compa ed o he lea e-one- ou me hod. In signal classifica ion, a no iceable de ail mus be e- membe ed. Va iables a e de e mined om peaks, and a signal consis s o one o mo e peaks. Thus, he da a gained om one signal usually includes se e al ows in an obse a ion ma ix. When defining aining and es se s, one needs o ensu e ha he whole da a om he signal is in ei he he aining o es se . Signal da a mus no be spli in o wo, such ha one pa is in aining se and he o he is in es se . In LOSO, he peak-based da a om each signal in u n o ms a es se , and he es o he da a a e in he aining se . When we ain a compu a ional model based on a classifica ion me hod, we mus emembe ha he aining phase o an algo i hm is pe o med on peak-based da a, no on signal le el da a. Hence, a classifie lea ns i s model based on peaks and no on signals. A e aining o a classifie , we gi e he es se as an inpu o he classifie . Then he classifie gi es a p edic ed class label o each peak in a es se . In his s age, esul s a e a peak le el. Howe e , since he aim o his pape is signal classifica ion, we need o ans o m he peak le el esul s in o a signal le el esul . This is done by aking a mode om he p e- dic ed class labels o peaks in a es se . We can do his because we use he LOSO p ocedu e, whe e a es se co e s da a only om one signal. Howe e , mode is no always unambiguously defined and a ie may occu . In ou pape we had only wo classes, so a ie could occu wi h only wo classes. I a ie occu ed, we sol ed he p oblem in he ol- lowing way. 1. Ex ac he aining da a o classes C 1 and C 2 occu ing in a ie, om he aining se . 2. Find he p opo ions P 1 =(|C 1 |/(|C 1 |+|C 2 |))100% and P 2 =(|C 2 |/ (|C 1 |+|C 2 |))100% whe e |.| is he size o a se . Hence, an in e al [0,P 1 ] is o he class C 1 and in e al (P 1 ,100] is o he class C 2 . 3. Gene a e a andom numbe R om he uni o m dis ibu ion U(0,1). 4. I R*100% belongs o in e al [0,P 1 ], selec C 1 as final class label o he signal. O he wise, selec C 2 . A e finding a p edic ed class label o each signal in a da ase , we can compa e he p edic ed class label wi h he ue label and cons uc a con usion ma ix. F om he con usion ma ix, we can e alua e di - e en kinds o measu es which desc ibe how well he classifica ion has succeeded. Fo ou s udy, we selec ed accu acy ((TP + TN)/ (TP + TN + FN + FP)), ue posi i e a e (TP/(TP + FN)) o diseases and ue nega i e a e (TN/(TN + FP)) o con ols. Fo he classifi- ca ion me hods which equi e pa ame e uning, we epea ed LOSO wi h all pa ame e alues examined, and selec ed a pa ame e alue (combina ion) ha achie ed he highes accu acy. 5. Classifica ion esul s o disease o con ol ansien signals The main a ge o he esea ch was o s udy how efficien ly wo ansien signal g oups can be diffe en ia ed om each o he . Fo his pu pose, se e al classifie s we e implemen ed as desc ibed abo e. Thei esul s a e p esen ed in he ollowing. Classifica ion esul s a e shown in Tables 2–4, in which ue posi i e a es (sensi i i y) co espond o disease ansien signals, and ue ne- ga i e a es (specifici y) o con ol signals. Accu acy equals he sum o ue posi i e and nega i e cases di ided by he numbe o all cases. Now knea es neighbo sea ching wi h ci yblock (Manha an) me ic yielded he bes accu acy esul s o 86.0% in Table 2.InTable 3,k nea es neighbo sea ching wi h Euclidean me ic and squa ed in e se weigh ing was he bes me hod, ob aining a 84.5% le el. In Table 4, he M. Juhola e al. In o ma ics in Medicine Unlocked 14 (2019) 15–22 19 suppo ec o machine (LSSVM) wi h adial basis unc ion (RBF) ke nel ha ing an accu acy o 84.7% and andom o es wi h 87.4% we e he bes echniques. O e all, he classifica ion o da a in o wo classes was e y success ul. 6. Discussion This s udy was aimed a in es iga ing whe he machine lea ning could, in gene al, sepa a e heal hy ca diomyocy es om diseased ones. The pheno ype o he iPSC-de i ed CMs was de e mined by Ca 2+ imaging. Bo h con ol and diseased ca diomyocy es con ained cells wi h no mal bea ing, as well as hose wi h abno mal bea ing beha io . Wi h machine lea ning, e y high classifica ion accu acy alues (up o 87.4%) we e ob ained o dis inguish con ol and diseased cells, despi e bo h ha ing mixed CM popula ions (con aining bo h no mal and ab- no mal CMs), i.e., o dis inguish CMs de i ed om pa ien s ca ying a mu a ion o a ca diac disease om con ol CMs. The iPSC echnology has e olu ionized he s udy o gene ic ca diac diseases [43]. I enables he in es iga ion o pa ien - and mu a ion- specific cells in o de o unde s and he disease pa hophysiology o in e es , as well as o p o ide a pla o m o s udy d ug esponsi eness in a pe sonalized way [4]. Howe e , CMs de i ed om iPSCs ob ained wi h cu en diffe en ia ion p o ocols s ill ha e se e al p oblems. They a e fi s o all imma u e ca diomyocy es [44]. In addi ion, hey p esen all ypes o ca diomyocy es including a ial, en icula and pacemake cells. These issues make i p oblema ic o p oduce and de e mine a disease pheno ype in a ep oducible way. In ou cu en s udy, hese limi a ions we e e iden . The CMs we e imma u e, and bo h ou con ol CMs as well as he diseased CMs we e mixed cell popula ions and wi h Ca 2+ ansien signals, e.g., en icula o a ial cells canno be dis- inguished om each o he . Howe e , despi e hese p oblems, ou machine lea ning p ocedu e was success ul o diffe en ia e con ol CMs om diseased CMs, sugges ing he p esence o cha ac e is ics o heal hy o diseased cells al eady in he a al s a e and common o all ypes o CMs. Due o abo e men ioned p oblems wi h iPSC-de i ed CM, con ol cells also included abno mal Ca 2+ ansien s. In ou s udy, 12.6% o con ol cells p esen ed a ious ypes o abno mali ies. Howe e , he amoun o abno mal ansien signals was much g ea e (54.6%) in CMs ca ying any mu a ion o a ious gene ic ca diac diseases including gene ic a hy hmias and ca diomyopa hies. We demons a ed ea lie [23] ha i is possible o diffe en ia e gene ic ca diac diseases om each o he based on machine lea ning echniques. In he cu en s udy, he aim was o collec mo e ansien signals o con ol CMs, o dec ease he diffe ence be ween he smalle signal numbe o con ol CMS and he g ea e signal numbe o diseased CMs compa ed o he ea lie si- ua ion [23], and pool all diseased ones as one g oup, o analyze whe he his could be sepa a ed om con ol cells con aining also bo h abno mally and no mally bea ing cells. Using diffe en algo i hms and me hods o machine lea ning, we we e able o p oduce pa adigms wi h high a classifica ion accu acy o up o 87.4%, sugges ing ha his p ocedu e could ha e po en ial use in clinical applica ions in he u u e. 7. Conclusions Gene ic ca diac diseases a e clinically o en p oblema ic. Fi s , he disease pheno ype is a iable e en wi hin a single amily [45]. Ad- di ionally, i is s ill common despi e ad ances in molecula gene ics ha he mu a ion causing he disease in he amily is no known. In his si ua ion, i is impossible o dis inguish hose who a e po en ially a isk o de eloping he disease pheno ype, which amily membe s should be egula ly checked clinically, and who should be ad ised o li es yle es ic ions o p e en i e medica ion. The esul s ob ained in his s udy a e po en ially p omising o iden i y indi iduals a isk. iPSC-de i ed CMs ca ying a disease causing mu a ion can accu a ely be sepa a ed om cells de i ed om heal hy indi iduals, and hus po en ially iPSC- de i ed ca diomyocy es combined wi h machine lea ning algo i hms could in he u u e be used also clinically o iden i y indi iduals a isk, Table 2 Classifica ion esul s o knea es neighbo (kNN) sea ching, wi h diffe en me ics o measu es wi h he bes k alue. Classifica ion me hod T ue posi i e a es o diseases % T ue posi i e a es o con ols % Accu acy % kNN wi h Chebyche me ic and equal weigh ing, k= 1 87.3 69.3 81.3 kNN wi h Chebyche me ic and in e se weigh ing, k= 1 87.3 69.3 81.3 kNN wi h Chebyche me ic and squa ed in e se weigh ing, k= 5 86.5 71.4 81.5 kNN wi h ci yblock me ic and equal weigh ing, k= 1 91.1 75.9 86.0 kNN wi h ci yblock me ic and in e se weigh ing, k= 1 91.1 75.9 86.0 kNN wi h ci yblock me ic and squa ed in e se weigh ing, k= 1 91.1 75.9 86.0 kNN wi h co ela ion measu e and equal weigh ing, k= 1 89.3 67.8 82.1 kNN wi h co ela ion measu e and in e se weigh ing, k= 5 89.3 71.4 83.3 kNN wi h co ela ion measu e and squa ed in e se weigh ing, k= 5 89.3 71.9 83.3 kNN wi h cosine measu e and equal weigh ing, k= 1 86.8 71.4 81.6 kNN wi h cosine measu e and in e se weigh ing, k= 5 87.8 74.4 83.3 kNN wi h cosine measu e and squa ed in e se weigh ing, k= 7 89.3 72.9 83.8 Table 3 Mo e classifica ion esul s o knea es neighbo (kNN) sea ching, wi h diffe en me ics o measu es wi h he bes k alue. Classifica ion me hod T ue posi i e a es o diseases % T ue nega i e a es o con ols % Accu acy % kNN wi h Euclidean me ic and equal weigh ing, k= 1 89.1 74.4 83.8 kNN wi h Euclidean me ic and in e se weigh ing, k= 1 89.1 74.4 84.1 kNN wi h Euclidean me ic and squa ed in e se weigh ing, k= 5 90.1 73.4 84.5 kNN wi h Mahalanobis me ic and equal weigh ing, k= 1 90.9 71.4 84.3 kNN wi h Mahalanobis me ic and in e se weigh ing, k= 1 90.9 71.4 84.3 kNN wi h Mahalanobis me ic and squa ed in e se weigh ing, k= 1 90.9 71.4 84.3 kNN wi h s anda dized Euclidean me ic and equal weigh ing. k= 1 89.1 74.4 84.1 kNN wi h s anda dized Euclidean me ic and in e se weigh ing, k= 1 89.1 74.4 84.1 kNN wi h s anda dized Euclidean me ic and squa ed in e se weigh ing, k= 5 89.8 73.4 84.3 kNN wi h Spea man measu e and equal weigh ing, k= 1 88.6 66.3 81.1 kNN wi h Spea man measu e and in e se weigh ing, k= 5 89.6 69.3 82.8 kNN wi h Spea man measu e and squa ed in e se weigh ing, k= 5 90.4 69.3 83.3 M. Juhola e al. In o ma ics in Medicine Unlocked 14 (2019) 15–22 20 and make i possible o ocus p e en i e ac ions on hose, and elie e he disease bu den om hose wi hou any signs o disease a he cel- lula le el. The high accu acy ob ained wi h ou bes machine lea ning algo- i hm sugges s ha iPSC echnology combined wi h machine lea ning could be used e en o diagnos ic pu poses in he u u e. We will con inue o collec mo e Ca 2+ ansien signals o CMs de i ed om a la ge collec ion o iPS cell lines ca ying diffe en mu a ions and pa- ien popula ions, as well as signals o heal hy con ols, and also om isogenic lines, and hus o imp o e hese me hods o make hem mo e sui able o clinical pu poses. Conflic s o in e es None. E hical s a emen The cu en s udy was app o ed by he E hics Commi ee o Pi kanmaa Hospi al Dis ic , Tampe e, Finland, in es ablishing, cul- u ing and diffe en ia ing human iPSC lines (R08070). Acknowledgmen s None. Appendix A. Supplemen a y da a Supplemen a y da a o his a icle can be ound online a h ps:// doi.o g/10.1016/j.imu.2019.01.006. Re e ences [1] Geo ge AL. Molecula and gene ic basis o sudden ca diac dea h. J Clin In es 2013;123:75–83. [2] Asa yan B, Medei os-Domingo A. T ansla ing eme ging molecula gene ic insigh s in o clinical p ac ice in inhe i ed ca diomyopa hies. J Mol Med 2018;96:993–1024. [3] Kujala K, Paa ola J, Leh i A, La sson K, Pekka inen-Ma ila M, Vii asalo M, Lah inen AM, Toi onen L, Kon ula K, Swan H, Laine M, Sil ennoinen O, Aal o-Se älä K. Cell model o ca echolamine cig polymo phic en icula achyca dia e eals ea ly and delayed a e depola iza ions. PLoS One 2012;7(9)h ps://doi.o g/10.1371/jou nal. pone.0044660. [4] Pen inen K, Swan H, Vanninen S, Paa ola J, Lah inen AM, Kon ula K, Aal o-Se älä K. An ia hy hmic effec s o Dan olene in pa ien s wi h ca echolamine gic poly- mo phic en icula achyca dia and eplica ion o he esponses using iPSC models. PLoS One 2015;10(7)h ps://doi.o g/10.1371/jou nal.pone.0125366. [5] Takahashi K, Tanabe K, Ohnuki M, Na i a M, Ichisaka T, Tomoda K, Yamanaka S. Induc ion o plu ipo en s em cells om adul human fib oblas s by defined ac o s. Cell 2007;131:861–72. [6] De alla HD, Passie R. Ca diac diffe en ia ion o plu ipo en s em cells and im- plica ions o modeling he hea in heal h and disease. Sci T ansl Med 2018;10. eaah5457. [7] Fa ima A, Xu G, Shao K, Papadopoulos S, Lehmann M, A naiz-Co JJ, Rosa AO, Nguemo F, Ma zkies M, Di mann S, S one SL, Linke M, Zechne U, Beye V, Hennies HC, Rosenk anz S, Klauke B, Pa wani AS, Ha e kamp W, Pfi ze G, Fa M, Cleemann L, Mo ad M, Mil ing H, Heschele J, Sa ic T. In i o modeling o ya- nodine ecep o 2 dys unc ion using human induced plu ipo en s em cells. Cell Physiol Biochem 2011;28:579–92. [8] Jung CB, Mo e i A, Mede os y Schni zle M, Iop L, S o ch U, Bellin M, Do n T, Ruppen hal S, P eiffe S, Goedel A, Di schinge RJ, Sey a h M, Lam JT, Sinnecke D, Gude mann T, Lipp P, Laugwi z KL. Dan olene escues a hy hmogenic RYR2 de ec in a pa ien -specific s em cell model o ca echolamine gic polymo phic en icula achyca dia. EMBO Mol Med 2012;4:180–91. [9] No ak A, Ba ad L, Lo be A, Ghe ghiceanu M, Rei e I, Eisen B, Eldo L, I sko i z- Eldo J, Elda M, A ad M, Binah O. Func ional abno mali ies in iPSC-de i ed ca - diomyocy es gene a ed om CPVT1 and CPVT2 pa ien s ca ying yanodine o calseques in mu a ions. J Cell Mol Med 2015;19:2006–18. [10] I zhaki I, Maizels L, Hube L, Geps ein A, A bel G, Caspi O, Mille L, Belhassen B, No E, Glikson M, Geps ein L. Modeling o ca echolamine gic polymo phic en- icula achyca dia wi h pa ien -specific human-induced plu ipo en s em cells. J Am Coll Ca diol 2012;60:990–1000. [11] Zhang XH, Ha iland S, Wei H, Sa ic T, Fa ima A, Heschele J, Cleemann L, Mo ad M. Ca 2+ signaling in human induced plu ipo en s em cell-de i ed ca diomyocy es (iPS-CM) om no mal and ca echolamine gic polymo phic en icula achyca dia (CPVT)-afflic ed subjec s. Cell Calcium 2013;54:57–70. [12] Di Pasquale E, Lodola F, Mi agoli M, Deneg i M, A elino-C uz JE, Buonoco e M, Nakahama H, Po a a o P, Bloise R, Napoli ano C, Condo elli G, P io i SG. CaMKII inhibi ion ec ifies a hy hmic pheno ype in a pa ien -specific model o ca echola- mine gic polymo phic en icula achyca dia. Cell Dea h Dis 2013;4:e843. [13] Mo e i A, Bellin M, Welling A, Jung CB, Lam JT, Bo -Flugel L, Do n T, Goedel A, Hohnke C, Ho mann F, Sey a h M, Sinnecke D, Schomig A, Laugwi z KL KL. Pa ien -specific induced plu ipo en s em-cell models o long-QT synd ome. N Engl J Med 2010;363:1397–409. [14] Ma sa E, Rajamohan D, Dick E, Young L, Mello L, S ani o h A, Denning C. D ug e alua ion in ca diomyocy es de i ed om human induced plu ipo en s em cells ca ying a long QT synd ome ype 2 mu a ion. Eu Hea J 2011;32:952–62. [15] Lah i AL, Kujala VJ, Chapman H, Koi is o AP, Pekkanen-Ma ila M, Ke kela E, Hy inen J, Kon ula K, Swan H, Conklin BR, Yamanaka S, Sil ennoinen O, Aal o- Se ala K. Model o long QT synd ome ype 2 using human iPS cells demons a es a hy hmogenic cha ac e is ics in cell cul u e. Dis. Model Mech. 2012;5:220–30. [16] Ki iaho AL, Ahola A, La sson K, Pen inen K, Swan H, Pekkanen-Ma ila M, Venäläinen H, Paa ola K, Hy inen J, Aal o-Se älä K. Dis inc elec ophysiological and mechanical bea ing pheno ypes o long QT synd ome ype 1-specific ca dio- myocy es ca ying diffe en mu a ions. IJC Hea & Vascula u e 2015;8:9–31. [17] Han L, Li Y, Tchao J, Kaplan AD, Lin B, Li Y, Mich-Basso J, Lis A, Hassan N, London B, Be CG, Tobi a K, Rasmusson RL, Yang L. S udy amilial hype ophic ca dio- myopa hy using pa ien -specific induced plu ipo en s em cells. Ca dio asc Res 2014;104(2):258–69. h ps://doi.o g/10.1093/c /c u205. Epub 2014 Sep 10. [18] Lan F, Lee AS, Liang P, Sanchez-F ei e V, Nguyen PK, Wang L, Han L, Yen M, Wang Y, Sun N, Abilez OJ, Hu S, Ebe AD, Na a e e EG, Simmons CS, Wheele M, P ui B, Lewis R, Yamaguchi Y, Ashley EA, Be s DM, Robbins RC, Longake MT, Wu JC. Abno mal calcium handling p ope ies unde lie amilial hype ophic ca diomyo- pa hy pa hology in pa ien -specific induced plu ipo en s em cells. Cell S em Cell 2013;12:101–13. [19] Ojala M, P ajapa i C, Pölönen RP, Rajala K, Pekkanen-Ma ila M, Rasku J, La sson K, Aal o-Se älä K. Mu a ion-specific pheno ypes in hiPSC-de i ed ca diomyocy es ca ying ei he myosin-binding p o ein C o α- opomyosin mu a ion o hype - ophic ca diomyopa hy. S em Cell In 2016:1684792h ps://www.hindawi.com/ jou nals/sci/2016/1684792/. [20] Lee EK, T an DD, Keung W, Chan P, Wong G, Chan CW, Cos a KD, Li RA, Khine M. Machine lea ning o human plu ipo en s em cell-de i ed enginee ed ca diac issue con ac ili y o au oma ed d ug classifica ion. S em Cell Rep 2017;9:1560–72. [21] Juhola M, Jou sijoki H, Va pa K, Saa ikoski J, Rasku J, Il anen K, Lau ikkala J, Hyy ö H, Á alos-Salgue o J, Sii ola H, Pen inen K, Aal o-Se älä K. On compu a ion Table 4 Resul s o disc iminan analysis, decision ee, mul inomial logis ic eg ession, naï e Bayes, andom o es and leas squa e (LS) suppo ec o machines (SVM). Classifica ion me hod T ue posi i e a es o diseases % T ue posi i e a es o con ols % Accu acy % Linea disc iminan analysis 78.4 62.3 73.0 Mahalanobis disc iminan analysis 33.0 94.0 53.5 Quad a ic disc iminan analysis 76.1 59.3 70.5 Decision ee 89.1 74.4 84.1 Mul inomial logis ic eg ession 77.7 62.8 72.7 Naï e Bayes wi h no mal dis ibu ion 71.8 67.8 70.5 Naï e Bayes wi h no mal ke nel 68.0 78.4 71.5 Naï e Bayes wi h box ke nel 66.5 79.4 70.8 Naï e Bayes wi h Epanechniko ke nel 67.5 78.9 71.3 Naï e Bayes wi h iangle ke nel 68.8 78.9 72.2 Random o es , numbe o ees 18 92.4 77.4 87.4 LS-SVM wi h linea ke nel, C=2 −4 69.3 76.4 71.7 LS-SVM wi h quad a ic ke nel, C= 1 74.6 77.9 75.7 LS-SVM wi h cubic ke nel, C=2 −5 79.4 78.4 79.1 LS-SVM wi h RBF ke nel, C=2 11 ,σ= 2 91.6 70.9 84.7 M. Juhola e al. In o ma ics in Medicine Unlocked 14 (2019) 15–22 21 o calcium cycling anomalies in ca diomyocy es da a. 36 h Annual In . Con . IEEE Eng. Med. Biol. Socie y. 2014. p. 1444–7. Chicago, Illinois, USA. [22] Juhola M, Pen inen K, Jou sijoki H, Va pa K, Saa ikoski J, Rasku J, Sii ola H, Il anen K, Lau ikkala J, Hyy ö H, Hy inen J, Aal o-Se älä K. Signal analysis and classifica ion me hods o calcium ansien da a o s em cell de i ed ca diomyo- cy es. Compu Biol Med 2015;61:1–7. [23] Juhola M, Jou sijoki H, Pen inen K, Aal o-Se älä K. De ec ion o gene ic ca diac diseases by Ca 2+ ansien p ofiles using machine lea ning me hods. Sci Rep 2018;8:9355h ps://doi.o g/10.1038/s41598-018-27695-5. [24] Han J, Kambe M, Pei J. Da a mining: concep s and echniques. hi d ed. Mo gan Kau mann; 2012. [25] Duda RO, Ha PE, S o k DG. Pa e n classifica ion. second ed. John Wiley & Sons; 2001. [26] Wu X, Kuma V, Quinlan R, Ghosh J, Yang Q, Mo oda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, S einbach M, Hand DJ, S einbe g D. Top 10 algo i hms in da a mining. Knowl In Sys 2008;14(1):1–37. [27] Tha wa A, Ta ek G, Abdelhameed I, Aboul Ella H. Linea disc iminan analysis: a de ailed u o ial. AI Commun 2017;30(2):169–90. [28] Cios KJ, Ped ycz W, Swinia ski RW, Ku gan LA. Da a mining: a knowledge dis- co e y app oach. Sp inge ; 2007. [29] Wu W, Malle Y, Walczak B, Penninckx W, Massa DL, Heue ding S, E ni F. Compa ison o egula ized disc iminan analysis, linea disc iminan analysis and quad a ic disc iminan analysis, applied o NIR da a. Anal Chim Ac a 1996;329(3):257–65. [30] G. Bohling, Classical no mal-based disc iminan analysis, Technical epo , Kansas Geol Su . h p://people.ku.edu/gbohling/EECS833 Accessed 26.7.2018. [31] Has ie T, Tibshi ani R, F iedmann J. The elemen s o s a is ical lea ning –da a mining, in e ence, and p edic ion. second ed. Sp inge ; 2009. [32] Ng AY, Jo dan MI. On disc imina i e s. gene a i e classifie s: a compa ison o logis ic eg ession and naï e Bayes NIPS. 2002. p. 841–8. [33] Ag es i A. Ca ego ical da a analysis. John Wiley & Sons; 1990. [34] Kwak C, Clay on-Ma hews A. Mul inomial logis ic eg ession. Nu s Res 2002;51(6):404–10. [35] Rokach L, Maimon O. Da a mining wi h decision ees: heo y and applica ions. Wo ld Scien ific; 2015. [36] B eiman L. Random o es s. Mach Lea n 2001;45(1):5–32. [37] Chen W, Xie X, Wang J, P adhan B, Hong H, Bui DT, Duan Z, Ma J. A compa a i e s udy o logis ic model ee, andom o es , and classifica ion and eg ession ee models o spa ial p edic ion o landslide suscep ibili y. Ca ena 2017;151:141–60. [38] Cou onné R, P obs P, Boules eix A-L. Random o es e sus logis ic eg ession: a la ge-scale benchma k expe imen . BMC Bioin 2018;19:270. [39] Co es C, Vapnik V. Suppo - ec o ne wo ks. Mach Lea n 1995;20(3):273–97. [40] Suykens JAK, Van Ges el T, De B aban e J, De Moo B, Vandewalle J. Leas squa es suppo ec o machines. Wo ld Scien ific; 1999. [41] Suykens JAK, Vandewalle J. Leas squa es suppo ec o machine classifie s. Neu al P ocess Le 1999;9(3):293–300. [42] Van Ges el T, Suykens JAK, Baesens B, Viaene S, Van hienen J, Dedene G, De Moo B, Vandewalle J. Benchma king leas squa es suppo ec o machine classifie s. Mach Lea n 2004;54(1):5–32. [43] Casini S, Ve ke k AO, Remme CA. Human iPSC-de i ed ca diomyocy es o in- es iga ion o disease mechanism and he apeu ic s a egies in inhe i ed a hy hmia synd omes: s eng hs and limi a ions. Ca dio asc D ugs The 2017;31:325–44. [44] Vee man CC, Kosmidis G, Mumme y CL, Casini S, Ve ke k AO, Bellin M. Inma u i y o human s em-cell-de i ed ca diomyocy es in cul u e: a al flaw o soluble p o- blem. S em Cell De 2015;24(9):1035–52. h ps://doi.o g/10.1089/scd.2014.0533. [45] Wilde AA, Beh ER. Gene ic es ing o inhe i ed ca diac disease. Na Re Ca diol 2013;10:571–83. M. Juhola e al. In o ma ics in Medicine Unlocked 14 (2019) 15–22 22