Dataset Information

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, namely drug-target protein, transcription factor-DNA element, and miRNA-mRNA. Also, validity of the novel predictions was evaluated by literature surveys. According to the results, the DIDL outperformed state-of-the-art methods. For all three networks, the areas under the curve and the precision-recall curve exceeded 0.85 and 0.83, respectively.

Conclusions

DIDL offers several advantages like automatic feature extraction from raw data, end-to-end training, and robustness to network sparsity. In addition, reliance solely on existing inter-layer interactions and independence of biochemical features of interacting molecules make this algorithm applicable for a wide variety of networks. DIDL paves the way to understand the underlying mechanisms of complex disorders through constructing integrative networks.

SUBMITTER: Borhani N

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

REPOSITORIES: biostudies-literature

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Publications

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

Borhani Niloofar N Ghaisari Jafar J Abedi Maryam M Kamali Marzieh M Gheisari Yousof Y

BMC bioinformatics 20220126 1

<h4>Background</h4>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.<h4>Results</h4>Here, as a novel nonlinear deep learning method, Data Integration with Deep Learning ( ...[more]

PMID: 35081903

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Dataset Information

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

Background

Results

Conclusions

Publications

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

Similar Datasets

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