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Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks

Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified comp...

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Autores principales: Palukuri, Meghana V., Marcotte, Edward M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240683/
https://www.ncbi.nlm.nih.gov/pubmed/34189530
http://dx.doi.org/10.1101/2021.06.22.449395
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author Palukuri, Meghana V.
Marcotte, Edward M.
author_facet Palukuri, Meghana V.
Marcotte, Edward M.
author_sort Palukuri, Meghana V.
collection PubMed
description Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.
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spelling pubmed-82406832021-06-30 Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks Palukuri, Meghana V. Marcotte, Edward M. bioRxiv Article Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0. Cold Spring Harbor Laboratory 2021-10-11 /pmc/articles/PMC8240683/ /pubmed/34189530 http://dx.doi.org/10.1101/2021.06.22.449395 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Palukuri, Meghana V.
Marcotte, Edward M.
Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks
title Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks
title_full Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks
title_fullStr Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks
title_full_unstemmed Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks
title_short Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks
title_sort super.complex: a supervised machine learning pipeline for molecular complex detection in protein-interaction networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240683/
https://www.ncbi.nlm.nih.gov/pubmed/34189530
http://dx.doi.org/10.1101/2021.06.22.449395
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