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Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network
Molecular components that are functionally interdependent in human cells constitute molecular association networks. Disease can be caused by disturbance of multiple molecular interactions. New biomolecular regulatory mechanisms can be revealed by discovering new biomolecular interactions. To this en...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317230/ https://www.ncbi.nlm.nih.gov/pubmed/32580123 http://dx.doi.org/10.1016/j.isci.2020.101261 |
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author | Yi, Hai-Cheng You, Zhu-Hong Huang, De-Shuang Guo, Zhen-Hao Chan, Keith C.C. Li, Yangming |
author_facet | Yi, Hai-Cheng You, Zhu-Hong Huang, De-Shuang Guo, Zhen-Hao Chan, Keith C.C. Li, Yangming |
author_sort | Yi, Hai-Cheng |
collection | PubMed |
description | Molecular components that are functionally interdependent in human cells constitute molecular association networks. Disease can be caused by disturbance of multiple molecular interactions. New biomolecular regulatory mechanisms can be revealed by discovering new biomolecular interactions. To this end, a heterogeneous molecular association network is formed by systematically integrating comprehensive associations between miRNAs, lncRNAs, circRNAs, mRNAs, proteins, drugs, microbes, and complex diseases. We propose a machine learning method for predicting intermolecular interactions, named MMI-Pred. More specifically, a network embedding model is developed to fully exploit the network behavior of biomolecules, and attribute features are also calculated. Then, these discriminative features are combined to train a random forest classifier to predict intermolecular interactions. MMI-Pred achieves an outstanding performance of 93.50% accuracy in hybrid associations prediction under 5-fold cross-validation. This work provides systematic landscape and machine learning method to model and infer complex associations between various biological components. |
format | Online Article Text |
id | pubmed-7317230 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-73172302020-06-30 Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network Yi, Hai-Cheng You, Zhu-Hong Huang, De-Shuang Guo, Zhen-Hao Chan, Keith C.C. Li, Yangming iScience Article Molecular components that are functionally interdependent in human cells constitute molecular association networks. Disease can be caused by disturbance of multiple molecular interactions. New biomolecular regulatory mechanisms can be revealed by discovering new biomolecular interactions. To this end, a heterogeneous molecular association network is formed by systematically integrating comprehensive associations between miRNAs, lncRNAs, circRNAs, mRNAs, proteins, drugs, microbes, and complex diseases. We propose a machine learning method for predicting intermolecular interactions, named MMI-Pred. More specifically, a network embedding model is developed to fully exploit the network behavior of biomolecules, and attribute features are also calculated. Then, these discriminative features are combined to train a random forest classifier to predict intermolecular interactions. MMI-Pred achieves an outstanding performance of 93.50% accuracy in hybrid associations prediction under 5-fold cross-validation. This work provides systematic landscape and machine learning method to model and infer complex associations between various biological components. Elsevier 2020-06-11 /pmc/articles/PMC7317230/ /pubmed/32580123 http://dx.doi.org/10.1016/j.isci.2020.101261 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Yi, Hai-Cheng You, Zhu-Hong Huang, De-Shuang Guo, Zhen-Hao Chan, Keith C.C. Li, Yangming Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network |
title | Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network |
title_full | Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network |
title_fullStr | Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network |
title_full_unstemmed | Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network |
title_short | Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network |
title_sort | learning representations to predict intermolecular interactions on large-scale heterogeneous molecular association network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317230/ https://www.ncbi.nlm.nih.gov/pubmed/32580123 http://dx.doi.org/10.1016/j.isci.2020.101261 |
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