WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.

Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across diff...

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Autores principales:	Bayarbaatar Amgalan, Hyunju Lee
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/87e77d52cee84432a48d08dcf61fd121
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id	oai:doaj.org-article:87e77d52cee84432a48d08dcf61fd121
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spelling	oai:doaj.org-article:87e77d52cee84432a48d08dcf61fd1212021-11-25T06:03:27ZWMAXC: a weighted maximum clique method for identifying condition-specific sub-network.1932-620310.1371/journal.pone.0104993https://doaj.org/article/87e77d52cee84432a48d08dcf61fd1212014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25148538/?tool=EBIhttps://doaj.org/toc/1932-6203Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC) method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function) over the standard simplex (sparsity constraints). We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.Bayarbaatar AmgalanHyunju LeePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 8, p e104993 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bayarbaatar Amgalan Hyunju Lee WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.
description	Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC) method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function) over the standard simplex (sparsity constraints). We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.
format	article
author	Bayarbaatar Amgalan Hyunju Lee
author_facet	Bayarbaatar Amgalan Hyunju Lee
author_sort	Bayarbaatar Amgalan
title	WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.
title_short	WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.
title_full	WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.
title_fullStr	WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.
title_full_unstemmed	WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.
title_sort	wmaxc: a weighted maximum clique method for identifying condition-specific sub-network.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/87e77d52cee84432a48d08dcf61fd121
work_keys_str_mv	AT bayarbaataramgalan wmaxcaweightedmaximumcliquemethodforidentifyingconditionspecificsubnetwork AT hyunjulee wmaxcaweightedmaximumcliquemethodforidentifyingconditionspecificsubnetwork
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WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.

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