Work

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Year: 2026

Type: article

Abstract: The exponential proliferation of cancer multi‐omics data offers unprecedented opportunities for precision oncology but presents severe challenges due to high dimensionality, sparsity, and heterogeneity. This article systematically reviews how artificial intelligence (AI) reshapes this landscape. First, we explore generative models (Variational Autoencoders, Generative Adversarial Networks) that reconstruct high‐fidelity data foundations through intelligent imputation and non‐linear batch correction. Second, we dissect the evolution of integration architectures, highlighting Graph Neural Networks for topological feature extraction and Transformers for constructing biological foundation models. Addressing clinical trust, we evaluate Explainable AI (XAI) strategies for transparent biomarker discovery. At the clinical level, we demonstrate AI's superior efficacy in molecular subtyping, survival stratification, and immunotherapy prediction. Finally, we identify meta‐learning, spatial multi‐omics, and federated learning as pivotal directions for realizing the clinical translation of next‐generation precision oncology.

Source: Advanced Intelligent Discovery

DOI: 10.1002/aidi.202500249

Authors: Shilong Liu, Shunxiang Li, Kun Qian

Institution: Shanghai Jiao Tong University

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: Science and Technology Commission of Shanghai Municipality