Table 2.

Application of AI in the Integration of Multi-omics.

Clinical application	Data	Model/Algorithm	Performance	References
cancer prognosis and survival prediction	RNA-Seq, Methylation, and miRNA	semi-supervised flexible hybrid machine-learning framework	Not applicable	Poirion, O.B., et al. (95)
breast cancer subtype identification	mRNA expression, miRNA expression and DNA methylation	deep learning fusion clustering framework	0.664	Shuangshuang, L., et al. (91)
cancer susceptibility prediction	copy number variations, miRNA expression, and gene expression	multimodal convolutional autoencoder model	0.9625	Karim, M.R., et al. (96)
identifying Neuroblastoma subtypes	gene expression, copy number alterations, Sequencing Quality Control project	deep learning	0.74	Zhang, L., et al. (97)
predict the survival of patients with lung cancer	TCGA	unsupervised learning	0.99	Takahashi, S., et al. (90)
survival stratification of gastric cancer	transcriptomics and epigenomics	bidirectional deep neural networks	0.76	Xu, J.M., et al. (98)
pan-cancer metastasis prediction	RNA-Seq, microRNA sequencing, and DNA methylation	deep learning	0.8885	Albaradei, S., et al. (92)
ovarian cancer subtypes identification	mRNA-seq, miRNA-seq, copy number variation, and the clinical information	deep learning	0.583	Guo, L. Y., et al. (99)
drug repurposing	copy number alteration, DNA methylation, gene expression, pharmacological characteristics for cancer cell lines	deep learning	0.84	Wang, Y., et al. (94)
predicting lung adenocarcinoma prognostication	mRNA, miRNA, DNA methylation and copy number variations	deep learning	0.65	Lee, T.-Y., et al. (100)
Diagnostic Classification of Lung Cancer	mRNA expression, miRNA-seq data, and DNA methylation data	deep transfer Learning	0.824	Zhu, R., et al. (101)
predicting effective therapeutic agents for breast cancer	copy number variations, miRNA, mutation, RNA, protein expression and methylation	deep learning	0.94	Khan, D. and S. Shedole (102)
predicting survival prognosis for glioma patients	transcription profile, miRNA expression, somatic mutations, copy number variation, DNA methylation, and protein expression	deep learning	0.990	Pan, X., et al. (103)
Diagnostic classification of cancers	mRNA expression, miRNA-seq, DNA methylation data and clinical information	XGBoost	0.595-0.872	Ma, B., et al. (104)
identify tumor molecular subtypes	copy number, mRNA, miRNA, DNA methylation and other omics data	consensus clustering and the Gaussian Mixture model	Not applicable	Yang, H., et al. (105)
predicting outcome for patients with hepatocellular carcinoma	DNA methylation and mRNA expression data	unsupervised machine-learning	Not applicable	Huang, G. J., et al. (106)
predicting the Gleason score levels of prostate cancer and the tumor stage in breast cancer	gene expression, DNA methylation, and copy number alteration	gene similarity network based on uniform manifold approximation and projection and convolutional neural networks	0.99	ElKarami, B., et al. (107)
patient classification, tumor grade classification, cancer subtype classification	mRNA expression, DNA methylation, and microRNA expression data	Multi-Omics Graph cOnvolutional NETworks	Not applicable	Wang, T. X., et al. (108)
cancer prognosis prediction	mRNA, miRNA, DNA methylation, and copy number variation	denoising Autoencoder	Not applicable	Chai, H., et al. (109)
cancer subtype classification	gene expression, miRNA expression and DNA methylation data	hierarchical integration deep flexible neural forest framework	0.885	Xu, J., et al. (110)
Prediction of prognosis of cancer	single nucleotide polymorphism, copy number variant, gene expression, and DNA methylation data	deep learning	0.67-0.88	Park, C., et al. (111)
tumor Stratification	deoxyribonucleic acid methylation, messenger ribonucleic acid expression data, and protein–protein interactions	Network Embedding; supervised learning; unsupervised clustering algorithm	0.91	Li, F., et al. (112)
discovery of cancer subtypes	mRNA expression, miRNA expression, DNA methylation, and copy number alterations	end-to-end variational deep learning-based clustering method; Variational Bayes	Not applicable	Rong, Z., et al. (113)