PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery

Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins...
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Autor*in:	Wei Wang [verfasserIn] Haiyan Yuan [verfasserIn] Junwei Han [verfasserIn] Wei Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Protein complex Deep proteomic data Group Lasso Logistic model Cancer classification

Übergeordnetes Werk:	In: Computational and Structural Biotechnology Journal - Elsevier, 2013, 21(2023), Seite 365-377
Übergeordnetes Werk:	volume:21 ; year:2023 ; pages:365-377

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.csbj.2022.12.005

Katalog-ID:	DOAJ003344711

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allfieldsSound	10.1016/j.csbj.2022.12.005 doi (DE-627)DOAJ003344711 (DE-599)DOAJd00fd5dbd74944298666db2d80775043 DE-627 ger DE-627 rakwb eng TP248.13-248.65 Wei Wang verfasserin aut PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts. Protein complex Deep proteomic data Group Lasso Logistic model Cancer classification Biotechnology Haiyan Yuan verfasserin aut Junwei Han verfasserin aut Wei Liu verfasserin aut In Computational and Structural Biotechnology Journal Elsevier, 2013 21(2023), Seite 365-377 (DE-627)731890086 (DE-600)2694435-2 20010370 nnns volume:21 year:2023 pages:365-377 https://doi.org/10.1016/j.csbj.2022.12.005 kostenfrei https://doaj.org/article/d00fd5dbd74944298666db2d80775043 kostenfrei http://www.sciencedirect.com/science/article/pii/S2001037022005621 kostenfrei https://doaj.org/toc/2001-0370 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2023 365-377
language	English
source	In Computational and Structural Biotechnology Journal 21(2023), Seite 365-377 volume:21 year:2023 pages:365-377
sourceStr	In Computational and Structural Biotechnology Journal 21(2023), Seite 365-377 volume:21 year:2023 pages:365-377
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Protein complex Deep proteomic data Group Lasso Logistic model Cancer classification Biotechnology
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container_title	Computational and Structural Biotechnology Journal
authorswithroles_txt_mv	Wei Wang @@aut@@ Haiyan Yuan @@aut@@ Junwei Han @@aut@@ Wei Liu @@aut@@
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callnumber-first	T - Technology
author	Wei Wang
spellingShingle	Wei Wang misc TP248.13-248.65 misc Protein complex misc Deep proteomic data misc Group Lasso misc Logistic model misc Cancer classification misc Biotechnology PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery
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topic_title	TP248.13-248.65 PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery Protein complex Deep proteomic data Group Lasso Logistic model Cancer classification
topic	misc TP248.13-248.65 misc Protein complex misc Deep proteomic data misc Group Lasso misc Logistic model misc Cancer classification misc Biotechnology
topic_unstemmed	misc TP248.13-248.65 misc Protein complex misc Deep proteomic data misc Group Lasso misc Logistic model misc Cancer classification misc Biotechnology
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title	PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery
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title_full	PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery
author_sort	Wei Wang
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title_sort	pclassolog: a protein complex-based, group lasso-logistic model for cancer classification and risk protein complex discovery
callnumber	TP248.13-248.65
title_auth	PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery
abstract	Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts.
abstractGer	Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts.
abstract_unstemmed	Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts.
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title_short	PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery
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PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery

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