Deep learning-based approaches for multi-omics data integration and analysis

• Published in: BioData mining Vol. 17; no. 1; pp. 38 - 29
• Main Authors: Ballard, Jenna L., Wang, Zexuan, Li, Wenrui, Shen, Li, Long, Qi
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02.10.2024; BioMed Central; BMC
• Subjects: Artificial neural networks; Biological analysis; Biomedical data; Clustering; Correlation analysis; Data analysis; Data integration; Datasets; Deep learning; Disease; Generative model; Genomics; Graph neural networks; Imaging; Integration; Knowledge representation; Machine learning; Medical imaging; Medical prognosis; Metabolomics; Multi-omics integration; Neural networks; Performance enhancement; Principal components analysis; Probability distribution; Proteomics; Radiomics; Review; Survival analysis; Task complexity; Transcriptomics; Variational methods; Deep learning; Multi-omics integration; Imaging; Generative model

The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a m...