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Revealing Missing Parts of the Interactome via Link Prediction

Protein interaction networks (PINs) are often used to “learn” new biological function from their topology. Since current PINs are noisy, their computational de-noising via link prediction (LP) could improve the learning accuracy. LP uses the existing PIN topology to predict missing and spurious link...

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Autores principales: Hulovatyy, Yuriy, Solava, Ryan W., Milenković, Tijana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3940777/
https://www.ncbi.nlm.nih.gov/pubmed/24594900
http://dx.doi.org/10.1371/journal.pone.0090073
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author Hulovatyy, Yuriy
Solava, Ryan W.
Milenković, Tijana
author_facet Hulovatyy, Yuriy
Solava, Ryan W.
Milenković, Tijana
author_sort Hulovatyy, Yuriy
collection PubMed
description Protein interaction networks (PINs) are often used to “learn” new biological function from their topology. Since current PINs are noisy, their computational de-noising via link prediction (LP) could improve the learning accuracy. LP uses the existing PIN topology to predict missing and spurious links. Many of existing LP methods rely on shared immediate neighborhoods of the nodes to be linked. As such, they have limitations. Thus, in order to comprehensively study what are the topological properties of nodes in PINs that dictate whether the nodes should be linked, we introduce novel sensitive LP measures that are expected to overcome the limitations of the existing methods. We systematically evaluate the new and existing LP measures by introducing “synthetic” noise into PINs and measuring how accurate the measures are in reconstructing the original PINs. Also, we use the LP measures to de-noise the original PINs, and we measure biological correctness of the de-noised PINs with respect to functional enrichment of the predicted interactions. Our main findings are: 1) LP measures that favor nodes which are both “topologically similar” and have large shared extended neighborhoods are superior; 2) using more network topology often though not always improves LP accuracy; and 3) LP improves biological correctness of the PINs, plus we validate a significant portion of the predicted interactions in independent, external PIN data sources. Ultimately, we are less focused on identifying a superior method but more on showing that LP improves biological correctness of PINs, which is its ultimate goal in computational biology. But we note that our new methods outperform each of the existing ones with respect to at least one evaluation criterion. Alarmingly, we find that the different criteria often disagree in identifying the best method(s), which has important implications for LP communities in any domain, including social networks.
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spelling pubmed-39407772014-03-06 Revealing Missing Parts of the Interactome via Link Prediction Hulovatyy, Yuriy Solava, Ryan W. Milenković, Tijana PLoS One Research Article Protein interaction networks (PINs) are often used to “learn” new biological function from their topology. Since current PINs are noisy, their computational de-noising via link prediction (LP) could improve the learning accuracy. LP uses the existing PIN topology to predict missing and spurious links. Many of existing LP methods rely on shared immediate neighborhoods of the nodes to be linked. As such, they have limitations. Thus, in order to comprehensively study what are the topological properties of nodes in PINs that dictate whether the nodes should be linked, we introduce novel sensitive LP measures that are expected to overcome the limitations of the existing methods. We systematically evaluate the new and existing LP measures by introducing “synthetic” noise into PINs and measuring how accurate the measures are in reconstructing the original PINs. Also, we use the LP measures to de-noise the original PINs, and we measure biological correctness of the de-noised PINs with respect to functional enrichment of the predicted interactions. Our main findings are: 1) LP measures that favor nodes which are both “topologically similar” and have large shared extended neighborhoods are superior; 2) using more network topology often though not always improves LP accuracy; and 3) LP improves biological correctness of the PINs, plus we validate a significant portion of the predicted interactions in independent, external PIN data sources. Ultimately, we are less focused on identifying a superior method but more on showing that LP improves biological correctness of PINs, which is its ultimate goal in computational biology. But we note that our new methods outperform each of the existing ones with respect to at least one evaluation criterion. Alarmingly, we find that the different criteria often disagree in identifying the best method(s), which has important implications for LP communities in any domain, including social networks. Public Library of Science 2014-03-03 /pmc/articles/PMC3940777/ /pubmed/24594900 http://dx.doi.org/10.1371/journal.pone.0090073 Text en © 2014 Hulovatyy et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Hulovatyy, Yuriy
Solava, Ryan W.
Milenković, Tijana
Revealing Missing Parts of the Interactome via Link Prediction
title Revealing Missing Parts of the Interactome via Link Prediction
title_full Revealing Missing Parts of the Interactome via Link Prediction
title_fullStr Revealing Missing Parts of the Interactome via Link Prediction
title_full_unstemmed Revealing Missing Parts of the Interactome via Link Prediction
title_short Revealing Missing Parts of the Interactome via Link Prediction
title_sort revealing missing parts of the interactome via link prediction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3940777/
https://www.ncbi.nlm.nih.gov/pubmed/24594900
http://dx.doi.org/10.1371/journal.pone.0090073
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