Обзорная статья

A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research

Flavio SartoriComputational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, ItalyFrancesco CodicèComputational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, ItalyIsabella CaranzanoComputational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, ItalyCesare RolloComputational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, ItalyGiovanni BiroloComputational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, ItalyPiero FariselliComputational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, ItalyCorrado PancottiComputational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126 Torino, Italy

2025en

ABI

Аннотация

The integration of deep learning (DL) with multi-omics data has significantly advanced our understanding of biological systems, particularly in cancer research. DL enables the analysis of high-dimensional datasets and the discovery of novel disease mechanisms and biomarkers, contributing to improved patient treatment and management. This review provides a detailed overview of recent developments in deep learning models applied to genomics data, with a focus on cancer type classification, driver gene identification, survival analysis, and drug response prediction. We introduce the foundational concepts of machine and deep learning and explain the characteristics of multi-omics data, addressing a broad and interdisciplinary audience. Methods published since 2020 are systematically reviewed, including their model architectures, datasets, and key innovations.

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Идентификаторы

DOI: 10.3390/genes16060648

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar