DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer
Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with me...
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Veröffentlicht in: | Bioinformatics (Oxford, England) England), 2021-04, Vol.37 (3), p.429-430 |
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creator | Li, Quanxue Dai, Wentao Liu, Jixiang Sang, Qingqing Li, Yi-Xue Li, Yuan-Yuan |
description | Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis.
The source code and user's guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig.
Supplementary data are available at Bioinformatics online. |
doi_str_mv | 10.1093/bioinformatics/btaa688 |
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The source code and user's guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig.
Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btaa688</identifier><identifier>PMID: 32717036</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Applications Notes ; Humans ; Machine Learning ; Neoplasms - genetics ; Software</subject><ispartof>Bioinformatics (Oxford, England), 2021-04, Vol.37 (3), p.429-430</ispartof><rights>The Author(s) 2020. Published by Oxford University Press.</rights><rights>The Author(s) 2020. Published by Oxford University Press. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c414t-54e133c9d5d891fc062f3c34cee13f46c4841c65aaae0cff7364f5ac0bf991a73</citedby><cites>FETCH-LOGICAL-c414t-54e133c9d5d891fc062f3c34cee13f46c4841c65aaae0cff7364f5ac0bf991a73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058765/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058765/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53770,53772</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32717036$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Luigi Martelli, Pier</contributor><creatorcontrib>Li, Quanxue</creatorcontrib><creatorcontrib>Dai, Wentao</creatorcontrib><creatorcontrib>Liu, Jixiang</creatorcontrib><creatorcontrib>Sang, Qingqing</creatorcontrib><creatorcontrib>Li, Yi-Xue</creatorcontrib><creatorcontrib>Li, Yuan-Yuan</creatorcontrib><title>DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer</title><title>Bioinformatics (Oxford, England)</title><addtitle>Bioinformatics</addtitle><description>Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis.
The source code and user's guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig.
Supplementary data are available at Bioinformatics online.</description><subject>Applications Notes</subject><subject>Humans</subject><subject>Machine Learning</subject><subject>Neoplasms - genetics</subject><subject>Software</subject><issn>1367-4803</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVUMtOwzAQtBCIQuEXKv9AqF0_knBAQuUpVUIqcI42jp0aEqeyU6T8Pa5aKnra1e7OzM4gNKHkhpKcTUvbWWc630JvVZiWPYDMshN0QZlME55RenroCRuhyxC-CCGCCHmORmyW0pQweYHswxCWun639S0Gh5d4Deobao0jN7aVdr01g3U1rrXTuBqC1_WmiaKdCxFQ4XJjm2p70Gq1AmdD_AcHWzvoN14HbB1W4JT2V-jMQBP09b6O0efT48f8JVm8Pb_O7xeJ4pT3ieCaMqbySlRZTo0icmaYYlzpODdcKp5xqqQAAE2UMSmT3AhQpDR5TiFlY3S3411vylZXKlrw0BRrb1vwQ9GBLY43zq6KuvspMiKyVIpIIHcEynch-jUHLCXFNvziOPxiH34ETv4rH2B_abNfnx6KwA</recordid><startdate>20210420</startdate><enddate>20210420</enddate><creator>Li, Quanxue</creator><creator>Dai, Wentao</creator><creator>Liu, Jixiang</creator><creator>Sang, Qingqing</creator><creator>Li, Yi-Xue</creator><creator>Li, Yuan-Yuan</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>5PM</scope></search><sort><creationdate>20210420</creationdate><title>DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer</title><author>Li, Quanxue ; Dai, Wentao ; Liu, Jixiang ; Sang, Qingqing ; Li, Yi-Xue ; Li, Yuan-Yuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c414t-54e133c9d5d891fc062f3c34cee13f46c4841c65aaae0cff7364f5ac0bf991a73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Applications Notes</topic><topic>Humans</topic><topic>Machine Learning</topic><topic>Neoplasms - genetics</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Quanxue</creatorcontrib><creatorcontrib>Dai, Wentao</creatorcontrib><creatorcontrib>Liu, Jixiang</creatorcontrib><creatorcontrib>Sang, Qingqing</creatorcontrib><creatorcontrib>Li, Yi-Xue</creatorcontrib><creatorcontrib>Li, Yuan-Yuan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Quanxue</au><au>Dai, Wentao</au><au>Liu, Jixiang</au><au>Sang, Qingqing</au><au>Li, Yi-Xue</au><au>Li, Yuan-Yuan</au><au>Luigi Martelli, Pier</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer</atitle><jtitle>Bioinformatics (Oxford, England)</jtitle><addtitle>Bioinformatics</addtitle><date>2021-04-20</date><risdate>2021</risdate><volume>37</volume><issue>3</issue><spage>429</spage><epage>430</epage><pages>429-430</pages><issn>1367-4803</issn><eissn>1367-4811</eissn><abstract>Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis.
The source code and user's guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig.
Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>32717036</pmid><doi>10.1093/bioinformatics/btaa688</doi><tpages>2</tpages><oa>free_for_read</oa></addata></record> |
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source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford Journals Open Access Collection; PubMed Central; Alma/SFX Local Collection |
subjects | Applications Notes Humans Machine Learning Neoplasms - genetics Software |
title | DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer |
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