Table 1.

Overview of different algorithms for multimodal data integration for biomarker and subtype prediction and clinical decision making. Methods are grouped by application tasks, characterized with a brief method description, as well as the specific data types they integrate.

Application	Method Description	Data Types					Reference
		Genetic	Proteogenomic	Epigenomic	Images	Clinical
Biomarker and Subtype Prediction
Prediction of aortic sizes and aortic disease risk	CNN	✓			✓		Pirruccello et al. [51]
Fine mapping of genetic loci	Probabilistic graphical model	✓		✓		✓	Ruffieux et al. [61]
Prediction of disease risk genes in Schizophrenia	Bayesian model	✓	✓	✓			Wang et al. [62]
Prediction of retinal related genes	CNN	✓			✓		Kirchler et al. [53]
Classification of breast cancer subtypes	DL, latent feature concatenation	✓	✓	✓			Lin et al. [37]
Classification of multiple disease subtypes	AE, uncertainty quantification		✓	✓			Han et al. [43]
Clinical Decision Making
Prediction of rare diseases	DL, SVM	✓			✓	✓	Hsieh et al. [56]
Classification of brain cancers	CNN, linear weighted module				✓		Yin et al. [73]
Prediction of the cancer origin of unknown primary	CNN, multiple instance learning				✓	✓	Lu et al. [78]
Classification of tumor type and survival prediction	AE, multi-task learning	✓	✓	✓			Zhang et al. [35]
Classification of multiple clinical outcomes	AE, latent feature averaging	✓	✓	✓			Tan et al. [40]
Classification of multiple clinical outcomes	AE, feature interaction network	✓	✓	✓		✓	Ma and Zhang [41]
Classification of patients with Alzheimer's disease	GCN, correlation discover network		✓	✓			Wang et al. [42]