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A deep learning approach to predict inter-omics interactions in multi-layer networks
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 strate...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8793231/ https://www.ncbi.nlm.nih.gov/pubmed/35081903 http://dx.doi.org/10.1186/s12859-022-04569-2 |
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author | Borhani, Niloofar Ghaisari, Jafar Abedi, Maryam Kamali, Marzieh Gheisari, Yousof |
author_facet | Borhani, Niloofar Ghaisari, Jafar Abedi, Maryam Kamali, Marzieh Gheisari, Yousof |
author_sort | Borhani, Niloofar |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04569-2. |
format | Online Article Text |
id | pubmed-8793231 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87932312022-02-03 A deep learning approach to predict inter-omics interactions in multi-layer networks Borhani, Niloofar Ghaisari, Jafar Abedi, Maryam Kamali, Marzieh Gheisari, Yousof BMC Bioinformatics Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04569-2. BioMed Central 2022-01-26 /pmc/articles/PMC8793231/ /pubmed/35081903 http://dx.doi.org/10.1186/s12859-022-04569-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Borhani, Niloofar Ghaisari, Jafar Abedi, Maryam Kamali, Marzieh Gheisari, Yousof A deep learning approach to predict inter-omics interactions in multi-layer networks |
title | A deep learning approach to predict inter-omics interactions in multi-layer networks |
title_full | A deep learning approach to predict inter-omics interactions in multi-layer networks |
title_fullStr | A deep learning approach to predict inter-omics interactions in multi-layer networks |
title_full_unstemmed | A deep learning approach to predict inter-omics interactions in multi-layer networks |
title_short | A deep learning approach to predict inter-omics interactions in multi-layer networks |
title_sort | deep learning approach to predict inter-omics interactions in multi-layer networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8793231/ https://www.ncbi.nlm.nih.gov/pubmed/35081903 http://dx.doi.org/10.1186/s12859-022-04569-2 |
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