A deep learning approach to predict inter-omics interactions in multi-layer networks.

ABSTRACT:

Background

Despite enormous achievements in the production of high-throughput datasets, constructing comprehensive maps of interactions remains a major challenge. Lack of sufficient experimental evidence on interactions is more significant for heterogeneous molecular types. Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease.

Results

Here, as a novel nonlinear deep learning method, Data Integration with Deep Learning (DIDL) was proposed to predict inter-omics interactions. It consisted of an encoder that performs automatic feature extraction for biomolecules according to existing interactions coupled with a predictor that predicts unforeseen interactions. Applicability of DIDL was assessed on different networks,

SUBMITTER: Borhani N 

PROVIDER: S-EPMC8793231 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature