Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess sev...

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Published in:PloS one 2015-11, Vol.10 (11), p.e0141869-e0141869
Main Authors: Park, Heewon, Imoto, Seiya, Miyano, Satoru
Format: Article
Language:English
Subjects:
Analysis
Antineoplastic Agents - pharmacology
Antineoplastic Agents - therapeutic use
Bioinformatics
Biological effects
Biomarkers
Breast cancer
Cancer
Cancer genetics
Colorectal cancer
Computational Biology - methods
Computer simulation
Data analysis
Data processing
Datasets
Dimensional analysis
Drug therapy
Epigenetics
Gene expression
Genes
Genomes
Genomics
Information management
Medical research
Molecular Targeted Therapy
Monte Carlo Method
Monte Carlo methods
Neoplasms - drug therapy
Neoplasms - genetics
Ovarian cancer
Parameter estimation
Regression analysis
Regularization
Regularization methods
Science
Sensitivity analysis
Statistical analysis
Statistics
Variables
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cited_by cdi_FETCH-LOGICAL-c758t-83cbe7ffbef5dd9a8bc60e9d91e04dcc59b450dfd5da9a40c4e276ce7828a5d63
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Imoto, Seiya
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description Uncovering driver genes is crucial for understanding heterogeneity in cancer. L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the "Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project" show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.
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L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the "Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project" show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0141869</identifier><identifier>PMID: 26544691</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Antineoplastic Agents - pharmacology ; Antineoplastic Agents - therapeutic use ; Bioinformatics ; Biological effects ; Biomarkers ; Breast cancer ; Cancer ; Cancer genetics ; Colorectal cancer ; Computational Biology - methods ; Computer simulation ; Data analysis ; Data processing ; Datasets ; Dimensional analysis ; Drug therapy ; Epigenetics ; Gene expression ; Genes ; Genomes ; Genomics ; Information management ; Medical research ; Molecular Targeted Therapy ; Monte Carlo Method ; Monte Carlo methods ; Neoplasms - drug therapy ; Neoplasms - genetics ; Ovarian cancer ; Parameter estimation ; Regression analysis ; Regularization ; Regularization methods ; Science ; Sensitivity analysis ; Statistical analysis ; Statistics ; Variables</subject><ispartof>PloS one, 2015-11, Vol.10 (11), p.e0141869-e0141869</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Park et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Park et al 2015 Park et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-83cbe7ffbef5dd9a8bc60e9d91e04dcc59b450dfd5da9a40c4e276ce7828a5d63</citedby><cites>FETCH-LOGICAL-c758t-83cbe7ffbef5dd9a8bc60e9d91e04dcc59b450dfd5da9a40c4e276ce7828a5d63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1731533173/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1731533173?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,74998</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26544691$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Cai, Xiaodong</contributor><creatorcontrib>Park, Heewon</creatorcontrib><creatorcontrib>Imoto, Seiya</creatorcontrib><creatorcontrib>Miyano, Satoru</creatorcontrib><title>Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Uncovering driver genes is crucial for understanding heterogeneity in cancer. L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. 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L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the "Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project" show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26544691</pmid><doi>10.1371/journal.pone.0141869</doi><oa>free_for_read</oa></addata></record>
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subjects Analysis
Antineoplastic Agents - pharmacology
Antineoplastic Agents - therapeutic use
Bioinformatics
Biological effects
Biomarkers
Breast cancer
Cancer
Cancer genetics
Colorectal cancer
Computational Biology - methods
Computer simulation
Data analysis
Data processing
Datasets
Dimensional analysis
Drug therapy
Epigenetics
Gene expression
Genes
Genomes
Genomics
Information management
Medical research
Molecular Targeted Therapy
Monte Carlo Method
Monte Carlo methods
Neoplasms - drug therapy
Neoplasms - genetics
Ovarian cancer
Parameter estimation
Regression analysis
Regularization
Regularization methods
Science
Sensitivity analysis
Statistical analysis
Statistics
Variables
title Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets
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