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Hyperbolic matrix factorization improves prediction of drug-target associations
Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the geometry of the biological space. By representing biological entities as points in a low dimensional Eu...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849222/ https://www.ncbi.nlm.nih.gov/pubmed/36653463 http://dx.doi.org/10.1038/s41598-023-27995-5 |
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author | Poleksic, Aleksandar |
author_facet | Poleksic, Aleksandar |
author_sort | Poleksic, Aleksandar |
collection | PubMed |
description | Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the geometry of the biological space. By representing biological entities as points in a low dimensional Euclidean space, state-of-the-art methods for drug-target interaction (DTI) prediction implicitly assume the flat geometry of the biological space. In contrast, recent theoretical studies suggest that biological systems exhibit tree-like topology with a high degree of clustering. As a consequence, embedding a biological system in a flat space leads to distortion of distances between biological objects. Here, we present a novel matrix factorization methodology for drug-target interaction prediction that uses hyperbolic space as the latent biological space. When benchmarked against classical, Euclidean methods, hyperbolic matrix factorization exhibits superior accuracy while lowering embedding dimension by an order of magnitude. We see this as additional evidence that the hyperbolic geometry underpins large biological networks. |
format | Online Article Text |
id | pubmed-9849222 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98492222023-01-20 Hyperbolic matrix factorization improves prediction of drug-target associations Poleksic, Aleksandar Sci Rep Article Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the geometry of the biological space. By representing biological entities as points in a low dimensional Euclidean space, state-of-the-art methods for drug-target interaction (DTI) prediction implicitly assume the flat geometry of the biological space. In contrast, recent theoretical studies suggest that biological systems exhibit tree-like topology with a high degree of clustering. As a consequence, embedding a biological system in a flat space leads to distortion of distances between biological objects. Here, we present a novel matrix factorization methodology for drug-target interaction prediction that uses hyperbolic space as the latent biological space. When benchmarked against classical, Euclidean methods, hyperbolic matrix factorization exhibits superior accuracy while lowering embedding dimension by an order of magnitude. We see this as additional evidence that the hyperbolic geometry underpins large biological networks. Nature Publishing Group UK 2023-01-18 /pmc/articles/PMC9849222/ /pubmed/36653463 http://dx.doi.org/10.1038/s41598-023-27995-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . |
spellingShingle | Article Poleksic, Aleksandar Hyperbolic matrix factorization improves prediction of drug-target associations |
title | Hyperbolic matrix factorization improves prediction of drug-target associations |
title_full | Hyperbolic matrix factorization improves prediction of drug-target associations |
title_fullStr | Hyperbolic matrix factorization improves prediction of drug-target associations |
title_full_unstemmed | Hyperbolic matrix factorization improves prediction of drug-target associations |
title_short | Hyperbolic matrix factorization improves prediction of drug-target associations |
title_sort | hyperbolic matrix factorization improves prediction of drug-target associations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849222/ https://www.ncbi.nlm.nih.gov/pubmed/36653463 http://dx.doi.org/10.1038/s41598-023-27995-5 |
work_keys_str_mv | AT poleksicaleksandar hyperbolicmatrixfactorizationimprovespredictionofdrugtargetassociations |