Applied Bionics and Biomechanics

Review Article

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

Table 2

Studies included in the GWAS-section analysis and AI methods applied.
ReferencesMethod of data analysisAI approaches
Musunuru et al. [10]SPSSMachine learning
Shi et al. [11]HAPGENTransfer learning
Li et al. [12]STAARMachine learning
Piechotta et al. [13]JACUSAMachine learning
Yazdani et al. [14]—Bayesian causal network
Depuydt et al. [15]Custom R scripts, SeuratMachine learning
Örd et al. [17]HOMERSupport vector regression
Aavik et al. [20]IngenuityMachine learning
Folkersen et al. [21]PLINKMachine learning
Plens-Galaska [22]GraphPad PrismMachine learning
Pérez-Sánchez [23]IngenuityMachine learning
Liu et al. [24]R packageMachine learning
Zekavat et al. [35]WGS, logistic regressionMachine learning
Nelson et al. [26]CARDIoGRAMplusC4DMachine learning
Manichaikul et al. [28]SMARTPCA, KINGMachine learning
Aherrahrou et al. [30]GraphPad PrismMachine learning
Aherrahrou et al. [31]PLINK, R package, GraphPad PrismMachine learning
Meng et al. [32]R package, CytoscapeMachine learning
Karjalainen et al. [34]CARDIoGRAMplusC4DMachine learning
Richardson et al. [37]CARDIoGRAMplusC4DMachine learning
Hoekstra et al. [36]PLINKMachine learning
Holliday et al. [38]PLINK, METALMachine learning
Awan et al. [39]R package, MCODEMachine learning
Lu et al. [40]LDhat package, METALMachine learning
Shendre et al. [41]LAMPLD, PLINKMachine learning
Shrestha et al. [42]PLINKMachine learning