Gene shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data
Gene shaving (GS) is an essential and challenging tools for biomedical researchers due to the large number of genes in human genome and the complex nature of biological networks. Most GS methods are not applicable to non-linear and multi-view data sets. While the kernel based methods can overcome th...
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| creator | Alam, Md Ashad Shahjaman, Mohammd Rahman, Md Ferdush Hossain, Fokhrul Deng, Hong-Wen |
| description | Gene shaving (GS) is an essential and challenging tools for biomedical researchers due to the large number of genes in human genome and the complex nature of biological networks. Most GS methods are not applicable to non-linear and multi-view data sets. While the kernel based methods can overcome these problems, a well-founded positive definite kernel based GS method has yet to be proposed for biomedical data analysis.
Since the kernel based methods on genomic information can improve the prediction of diseases, here we proposed a noble method, "kernel based gene shaving" which is based on the influence function of kernel canonical correlation analysis. To investigate the performance of the proposed method in comparison to state-of-the-art-method in gene saving, we analyzed extensive simulated and real microarray gene expression data set. The performance metrics including true positive rate, true negative rate, false positive rate, false negative rate, misclassification error rate, the false discovery rate and area under curves were computed for each methods. In colon cancer data analysis, the proposed method identified a significant subsets of 210 genes out of 2000 genes and suggestive superior performance compared with other methods. The proposed method can be applied to the study of other disease process where two view data is a common task.
We addressed the challenge of finding unique kernel based GS methods by using the influence function of kernel canonical correlation analysis. The proposed method has shown to have better performance than state-of-the-art-methods in gene saving and has identified many more significant gene interactions, suggesting that genes function in a concerted effort in colon cancer. In similar biomedical data analysis, kernel based methods could be applied to select a potential subset of genes. The positive definite kernel based methods can overcome the non-linearity problem and improve the prediction process. |
| doi_str_mv | 10.1371/journal.pone.0217027 |
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Since the kernel based methods on genomic information can improve the prediction of diseases, here we proposed a noble method, "kernel based gene shaving" which is based on the influence function of kernel canonical correlation analysis. To investigate the performance of the proposed method in comparison to state-of-the-art-method in gene saving, we analyzed extensive simulated and real microarray gene expression data set. The performance metrics including true positive rate, true negative rate, false positive rate, false negative rate, misclassification error rate, the false discovery rate and area under curves were computed for each methods. In colon cancer data analysis, the proposed method identified a significant subsets of 210 genes out of 2000 genes and suggestive superior performance compared with other methods. The proposed method can be applied to the study of other disease process where two view data is a common task.
We addressed the challenge of finding unique kernel based GS methods by using the influence function of kernel canonical correlation analysis. The proposed method has shown to have better performance than state-of-the-art-methods in gene saving and has identified many more significant gene interactions, suggesting that genes function in a concerted effort in colon cancer. In similar biomedical data analysis, kernel based methods could be applied to select a potential subset of genes. The positive definite kernel based methods can overcome the non-linearity problem and improve the prediction process.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0217027</identifier><identifier>PMID: 31120939</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Area Under Curve ; Artificial Intelligence ; Bioinformatics ; Biology and Life Sciences ; Biomedical data ; Cancer ; Colon ; Colon cancer ; Colonic Neoplasms - diagnosis ; Colonic Neoplasms - genetics ; Colorectal cancer ; Computational Biology ; Computer Simulation ; Correlation ; Correlation analysis ; Data analysis ; Datasets ; DNA microarrays ; False Positive Reactions ; Gene expression ; Gene Expression Profiling ; Genes ; Genetic engineering ; Genetic Techniques ; Genomes ; Genomics ; Hilbert space ; Humans ; Identification methods ; Influence functions ; Information processing ; Kernel functions ; Kernels ; Learning algorithms ; Linearity ; Machine Learning ; Medical research ; Medicine and Health Sciences ; Methods ; Nonlinear Dynamics ; Nonlinearity ; Normal distribution ; Oligonucleotide Array Sequence Analysis ; Performance measurement ; Physical Sciences ; Principal components analysis ; Research and Analysis Methods ; Sensitivity analysis ; Shaving ; Software ; Technology application</subject><ispartof>PloS one, 2019-05, Vol.14 (5), p.e0217027</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Alam 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>2019 Alam et al 2019 Alam et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-6e7451c1b23635a539d584aa11c5bf68e03a0ebcbc9f613f94c96ee5e0b9a7c53</citedby><cites>FETCH-LOGICAL-c692t-6e7451c1b23635a539d584aa11c5bf68e03a0ebcbc9f613f94c96ee5e0b9a7c53</cites><orcidid>0000-0002-7622-0216</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6532884/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6532884/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53769,53771,79346,79347</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31120939$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Hernandez-Lemus, Enrique</contributor><creatorcontrib>Alam, Md Ashad</creatorcontrib><creatorcontrib>Shahjaman, Mohammd</creatorcontrib><creatorcontrib>Rahman, Md Ferdush</creatorcontrib><creatorcontrib>Hossain, Fokhrul</creatorcontrib><creatorcontrib>Deng, Hong-Wen</creatorcontrib><title>Gene shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Gene shaving (GS) is an essential and challenging tools for biomedical researchers due to the large number of genes in human genome and the complex nature of biological networks. Most GS methods are not applicable to non-linear and multi-view data sets. While the kernel based methods can overcome these problems, a well-founded positive definite kernel based GS method has yet to be proposed for biomedical data analysis.
Since the kernel based methods on genomic information can improve the prediction of diseases, here we proposed a noble method, "kernel based gene shaving" which is based on the influence function of kernel canonical correlation analysis. To investigate the performance of the proposed method in comparison to state-of-the-art-method in gene saving, we analyzed extensive simulated and real microarray gene expression data set. The performance metrics including true positive rate, true negative rate, false positive rate, false negative rate, misclassification error rate, the false discovery rate and area under curves were computed for each methods. In colon cancer data analysis, the proposed method identified a significant subsets of 210 genes out of 2000 genes and suggestive superior performance compared with other methods. The proposed method can be applied to the study of other disease process where two view data is a common task.
We addressed the challenge of finding unique kernel based GS methods by using the influence function of kernel canonical correlation analysis. The proposed method has shown to have better performance than state-of-the-art-methods in gene saving and has identified many more significant gene interactions, suggesting that genes function in a concerted effort in colon cancer. In similar biomedical data analysis, kernel based methods could be applied to select a potential subset of genes. The positive definite kernel based methods can overcome the non-linearity problem and improve the prediction process.</description><subject>Algorithms</subject><subject>Area Under Curve</subject><subject>Artificial Intelligence</subject><subject>Bioinformatics</subject><subject>Biology and Life Sciences</subject><subject>Biomedical data</subject><subject>Cancer</subject><subject>Colon</subject><subject>Colon cancer</subject><subject>Colonic Neoplasms - diagnosis</subject><subject>Colonic Neoplasms - genetics</subject><subject>Colorectal cancer</subject><subject>Computational Biology</subject><subject>Computer Simulation</subject><subject>Correlation</subject><subject>Correlation analysis</subject><subject>Data analysis</subject><subject>Datasets</subject><subject>DNA microarrays</subject><subject>False Positive Reactions</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Genes</subject><subject>Genetic engineering</subject><subject>Genetic Techniques</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Hilbert space</subject><subject>Humans</subject><subject>Identification methods</subject><subject>Influence functions</subject><subject>Information processing</subject><subject>Kernel functions</subject><subject>Kernels</subject><subject>Learning algorithms</subject><subject>Linearity</subject><subject>Machine Learning</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Nonlinear Dynamics</subject><subject>Nonlinearity</subject><subject>Normal distribution</subject><subject>Oligonucleotide Array Sequence Analysis</subject><subject>Performance measurement</subject><subject>Physical Sciences</subject><subject>Principal components analysis</subject><subject>Research and Analysis Methods</subject><subject>Sensitivity analysis</subject><subject>Shaving</subject><subject>Software</subject><subject>Technology 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shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data</title><author>Alam, Md Ashad ; Shahjaman, Mohammd ; Rahman, Md Ferdush ; Hossain, Fokhrul ; Deng, Hong-Wen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-6e7451c1b23635a539d584aa11c5bf68e03a0ebcbc9f613f94c96ee5e0b9a7c53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Area Under Curve</topic><topic>Artificial Intelligence</topic><topic>Bioinformatics</topic><topic>Biology and Life Sciences</topic><topic>Biomedical data</topic><topic>Cancer</topic><topic>Colon</topic><topic>Colon cancer</topic><topic>Colonic Neoplasms - diagnosis</topic><topic>Colonic Neoplasms - genetics</topic><topic>Colorectal cancer</topic><topic>Computational Biology</topic><topic>Computer Simulation</topic><topic>Correlation</topic><topic>Correlation 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Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alam, Md Ashad</au><au>Shahjaman, Mohammd</au><au>Rahman, Md Ferdush</au><au>Hossain, Fokhrul</au><au>Deng, Hong-Wen</au><au>Hernandez-Lemus, Enrique</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gene shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-05-23</date><risdate>2019</risdate><volume>14</volume><issue>5</issue><spage>e0217027</spage><pages>e0217027-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Gene shaving (GS) is an essential and challenging tools for biomedical researchers due to the large number of genes in human genome and the complex nature of biological networks. Most GS methods are not applicable to non-linear and multi-view data sets. While the kernel based methods can overcome these problems, a well-founded positive definite kernel based GS method has yet to be proposed for biomedical data analysis.
Since the kernel based methods on genomic information can improve the prediction of diseases, here we proposed a noble method, "kernel based gene shaving" which is based on the influence function of kernel canonical correlation analysis. To investigate the performance of the proposed method in comparison to state-of-the-art-method in gene saving, we analyzed extensive simulated and real microarray gene expression data set. The performance metrics including true positive rate, true negative rate, false positive rate, false negative rate, misclassification error rate, the false discovery rate and area under curves were computed for each methods. In colon cancer data analysis, the proposed method identified a significant subsets of 210 genes out of 2000 genes and suggestive superior performance compared with other methods. The proposed method can be applied to the study of other disease process where two view data is a common task.
We addressed the challenge of finding unique kernel based GS methods by using the influence function of kernel canonical correlation analysis. The proposed method has shown to have better performance than state-of-the-art-methods in gene saving and has identified many more significant gene interactions, suggesting that genes function in a concerted effort in colon cancer. In similar biomedical data analysis, kernel based methods could be applied to select a potential subset of genes. The positive definite kernel based methods can overcome the non-linearity problem and improve the prediction process.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31120939</pmid><doi>10.1371/journal.pone.0217027</doi><tpages>e0217027</tpages><orcidid>https://orcid.org/0000-0002-7622-0216</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Area Under Curve Artificial Intelligence Bioinformatics Biology and Life Sciences Biomedical data Cancer Colon Colon cancer Colonic Neoplasms - diagnosis Colonic Neoplasms - genetics Colorectal cancer Computational Biology Computer Simulation Correlation Correlation analysis Data analysis Datasets DNA microarrays False Positive Reactions Gene expression Gene Expression Profiling Genes Genetic engineering Genetic Techniques Genomes Genomics Hilbert space Humans Identification methods Influence functions Information processing Kernel functions Kernels Learning algorithms Linearity Machine Learning Medical research Medicine and Health Sciences Methods Nonlinear Dynamics Nonlinearity Normal distribution Oligonucleotide Array Sequence Analysis Performance measurement Physical Sciences Principal components analysis Research and Analysis Methods Sensitivity analysis Shaving Software Technology application |
| title | Gene shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T05%3A58%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Gene%20shaving%20using%20a%20sensitivity%20analysis%20of%20kernel%20based%20machine%20learning%20approach,%20with%20applications%20to%20cancer%20data&rft.jtitle=PloS%20one&rft.au=Alam,%20Md%20Ashad&rft.date=2019-05-23&rft.volume=14&rft.issue=5&rft.spage=e0217027&rft.pages=e0217027-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0217027&rft_dat=%3Cgale_plos_%3EA586423538%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2229632999&rft_id=info:pmid/31120939&rft_galeid=A586423538&rft_doaj_id=oai_doaj_org_article_70b2cf600bec478d9379e856c776a328&rfr_iscdi=true |