CustOmics: A versatile deep-learning based strategy for multi-omics integration.

ABSTRACT: The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease's underlying biological processes and developing predictive models. It also comes with new challenges in computational biology in terms of integrating high-dimensional and heterogeneous data in a fashion that captures the interrelationships between multiple genes and their functions. Deep learning methods offer promising perspectives for integrating multi-omics data. In this paper, we review the existing integration strategies based on autoencoders and propose a new customizable one whose principle relies on a two-phase approach. In the first phase, we adapt the training to each data source independently before learning cross-modality interactions in the second phase. By ta

SUBMITTER: Benkirane H 

PROVIDER: S-EPMC10019780 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature