Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models
Detecting divergence between oncogenic tumors plays a pivotal role in cancer diagnosis and therapy. This research work was focused on designing a computational strategy to predict the class of lung cancer tumors from the structural and physicochemical properties (1497 attributes) of protein sequence...
Gespeichert in:
| Veröffentlicht in: | PloS one 2013-03, Vol.8 (3), p.e58772-e58772 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e58772 |
|---|---|
| container_issue | 3 |
| container_start_page | e58772 |
| container_title | PloS one |
| container_volume | 8 |
| creator | Ramani, R Geetha Jacob, Shomona Gracia |
| description | Detecting divergence between oncogenic tumors plays a pivotal role in cancer diagnosis and therapy. This research work was focused on designing a computational strategy to predict the class of lung cancer tumors from the structural and physicochemical properties (1497 attributes) of protein sequences obtained from genes defined by microarray analysis. The proposed methodology involved the use of hybrid feature selection techniques (gain ratio and correlation based subset evaluators with Incremental Feature Selection) followed by Bayesian Network prediction to discriminate lung cancer tumors as Small Cell Lung Cancer (SCLC), Non-Small Cell Lung Cancer (NSCLC) and the COMMON classes. Moreover, this methodology eliminated the need for extensive data cleansing strategies on the protein properties and revealed the optimal and minimal set of features that contributed to lung cancer tumor classification with an improved accuracy compared to previous work. We also attempted to predict via supervised clustering the possible clusters in the lung tumor data. Our results revealed that supervised clustering algorithms exhibited poor performance in differentiating the lung tumor classes. Hybrid feature selection identified the distribution of solvent accessibility, polarizability and hydrophobicity as the highest ranked features with Incremental feature selection and Bayesian Network prediction generating the optimal Jack-knife cross validation accuracy of 87.6%. Precise categorization of oncogenic genes causing SCLC and NSCLC based on the structural and physicochemical properties of their protein sequences is expected to unravel the functionality of proteins that are essential in maintaining the genomic integrity of a cell and also act as an informative source for drug design, targeting essential protein properties and their composition that are found to exist in lung cancer tumors. |
| doi_str_mv | 10.1371/journal.pone.0058772 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1330883021</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A478394080</galeid><doaj_id>oai_doaj_org_article_7ec57f2330024a3d87dfe0ca54d004de</doaj_id><sourcerecordid>A478394080</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-15c1d4a7ec1a763404f6b6237d77064c943f08f57ad5a2159df13fe91ee9d3653</originalsourceid><addsrcrecordid>eNqNk12L1DAUhoso7rr6D0QLgujFjEnTNO2NsCx-DCws-HUbMsnJTIY0GZN0cX-A_9vU6S5T2QvpRfPxvG_OOckpiucYLTFh-N3OD8EJu9x7B0uEaMtY9aA4xR2pFk2FyMOj8UnxJMZdhkjbNI-Lk4pQRCntTovfq34f_DWoUloRo9FGimS8K70u7eA2pRROQijT0PsQy7WIGc3bMYVBpiEIWwqnyv32Jhrp5Rb6bGDL7LmHkAzE0SjPEhgXyyGabKlEEmVv3DjuvQIbnxaPtLARnk3_s-L7xw_fLj4vLq8-rS7OLxey6aq0wFRiVQsGEgvWkBrVulk3FWGKMdTUsquJRq2mTCgqKkw7pTHR0GGATpGGkrPi5cF3b33kUwUjx4SgtiWowplYHQjlxY7vg-lFuOFeGP53wYcNFzkvaYHnMCjTVdaiqhZEtUxpQFLQWiFUK8he76fThnUPSoJLuV4z0_mOM1u-8dec0A6TdgzmzWQQ_M8BYuK9iRKsFQ78MMaNWUurDjcZffUPen92E7UROQHjtM_nytGUn9esJV2NWpSp5T1U_tR4u_m5aZPXZ4K3M0FmEvxKGzHEyFdfv_w_e_Vjzr4-YrcgbNpGb4fxgcY5WB9AGXyMAfRdkTHiY7fcVoOP3cKnbsmyF8cXdCe6bQ_yB2CLEc4</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1330883021</pqid></control><display><type>article</type><title>Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Ramani, R Geetha ; Jacob, Shomona Gracia</creator><contributor>Uversky, Vladimir N.</contributor><creatorcontrib>Ramani, R Geetha ; Jacob, Shomona Gracia ; Uversky, Vladimir N.</creatorcontrib><description>Detecting divergence between oncogenic tumors plays a pivotal role in cancer diagnosis and therapy. This research work was focused on designing a computational strategy to predict the class of lung cancer tumors from the structural and physicochemical properties (1497 attributes) of protein sequences obtained from genes defined by microarray analysis. The proposed methodology involved the use of hybrid feature selection techniques (gain ratio and correlation based subset evaluators with Incremental Feature Selection) followed by Bayesian Network prediction to discriminate lung cancer tumors as Small Cell Lung Cancer (SCLC), Non-Small Cell Lung Cancer (NSCLC) and the COMMON classes. Moreover, this methodology eliminated the need for extensive data cleansing strategies on the protein properties and revealed the optimal and minimal set of features that contributed to lung cancer tumor classification with an improved accuracy compared to previous work. We also attempted to predict via supervised clustering the possible clusters in the lung tumor data. Our results revealed that supervised clustering algorithms exhibited poor performance in differentiating the lung tumor classes. Hybrid feature selection identified the distribution of solvent accessibility, polarizability and hydrophobicity as the highest ranked features with Incremental feature selection and Bayesian Network prediction generating the optimal Jack-knife cross validation accuracy of 87.6%. Precise categorization of oncogenic genes causing SCLC and NSCLC based on the structural and physicochemical properties of their protein sequences is expected to unravel the functionality of proteins that are essential in maintaining the genomic integrity of a cell and also act as an informative source for drug design, targeting essential protein properties and their composition that are found to exist in lung cancer tumors.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0058772</identifier><identifier>PMID: 23505559</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Analysis ; Artificial intelligence ; Bayes Theorem ; Bayesian analysis ; Bioinformatics ; Biology ; Biomarkers ; Cancer ; Carcinoma, Non-Small-Cell Lung - classification ; Classification ; Cleaning ; Cluster Analysis ; Clustering ; Computer applications ; Computer Science ; Data mining ; Data Mining - methods ; Data processing ; Databases, Genetic ; Diagnosis ; Discriminant analysis ; Divergence ; DNA methylation ; DNA microarrays ; Drug development ; Gene expression ; Gene sequencing ; Genes ; Genetic counseling ; Genomics ; Health aspects ; Humans ; Hydrophobicity ; Lung cancer ; Lung diseases ; Lung Neoplasms - classification ; Mathematical models ; Medical Informatics - methods ; Medicine ; Non-small cell lung cancer ; Non-small cell lung carcinoma ; Physicochemical properties ; Polarizability ; Properties (attributes) ; Protein composition ; Proteins ; Proteins - chemistry ; Reproducibility of Results ; Tumors</subject><ispartof>PloS one, 2013-03, Vol.8 (3), p.e58772-e58772</ispartof><rights>COPYRIGHT 2013 Public Library of Science</rights><rights>2013 Ramani, Jacob. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://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>2013 Ramani, Jacob 2013 Ramani, Jacob</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-15c1d4a7ec1a763404f6b6237d77064c943f08f57ad5a2159df13fe91ee9d3653</citedby><cites>FETCH-LOGICAL-c692t-15c1d4a7ec1a763404f6b6237d77064c943f08f57ad5a2159df13fe91ee9d3653</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/PMC3591381/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591381/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23505559$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Uversky, Vladimir N.</contributor><creatorcontrib>Ramani, R Geetha</creatorcontrib><creatorcontrib>Jacob, Shomona Gracia</creatorcontrib><title>Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Detecting divergence between oncogenic tumors plays a pivotal role in cancer diagnosis and therapy. This research work was focused on designing a computational strategy to predict the class of lung cancer tumors from the structural and physicochemical properties (1497 attributes) of protein sequences obtained from genes defined by microarray analysis. The proposed methodology involved the use of hybrid feature selection techniques (gain ratio and correlation based subset evaluators with Incremental Feature Selection) followed by Bayesian Network prediction to discriminate lung cancer tumors as Small Cell Lung Cancer (SCLC), Non-Small Cell Lung Cancer (NSCLC) and the COMMON classes. Moreover, this methodology eliminated the need for extensive data cleansing strategies on the protein properties and revealed the optimal and minimal set of features that contributed to lung cancer tumor classification with an improved accuracy compared to previous work. We also attempted to predict via supervised clustering the possible clusters in the lung tumor data. Our results revealed that supervised clustering algorithms exhibited poor performance in differentiating the lung tumor classes. Hybrid feature selection identified the distribution of solvent accessibility, polarizability and hydrophobicity as the highest ranked features with Incremental feature selection and Bayesian Network prediction generating the optimal Jack-knife cross validation accuracy of 87.6%. Precise categorization of oncogenic genes causing SCLC and NSCLC based on the structural and physicochemical properties of their protein sequences is expected to unravel the functionality of proteins that are essential in maintaining the genomic integrity of a cell and also act as an informative source for drug design, targeting essential protein properties and their composition that are found to exist in lung cancer tumors.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial intelligence</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bioinformatics</subject><subject>Biology</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Carcinoma, Non-Small-Cell Lung - classification</subject><subject>Classification</subject><subject>Cleaning</subject><subject>Cluster Analysis</subject><subject>Clustering</subject><subject>Computer applications</subject><subject>Computer Science</subject><subject>Data mining</subject><subject>Data Mining - methods</subject><subject>Data processing</subject><subject>Databases, Genetic</subject><subject>Diagnosis</subject><subject>Discriminant analysis</subject><subject>Divergence</subject><subject>DNA methylation</subject><subject>DNA microarrays</subject><subject>Drug development</subject><subject>Gene expression</subject><subject>Gene sequencing</subject><subject>Genes</subject><subject>Genetic counseling</subject><subject>Genomics</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Hydrophobicity</subject><subject>Lung cancer</subject><subject>Lung diseases</subject><subject>Lung Neoplasms - classification</subject><subject>Mathematical models</subject><subject>Medical Informatics - methods</subject><subject>Medicine</subject><subject>Non-small cell lung cancer</subject><subject>Non-small cell lung carcinoma</subject><subject>Physicochemical properties</subject><subject>Polarizability</subject><subject>Properties (attributes)</subject><subject>Protein composition</subject><subject>Proteins</subject><subject>Proteins - chemistry</subject><subject>Reproducibility of Results</subject><subject>Tumors</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk12L1DAUhoso7rr6D0QLgujFjEnTNO2NsCx-DCws-HUbMsnJTIY0GZN0cX-A_9vU6S5T2QvpRfPxvG_OOckpiucYLTFh-N3OD8EJu9x7B0uEaMtY9aA4xR2pFk2FyMOj8UnxJMZdhkjbNI-Lk4pQRCntTovfq34f_DWoUloRo9FGimS8K70u7eA2pRROQijT0PsQy7WIGc3bMYVBpiEIWwqnyv32Jhrp5Rb6bGDL7LmHkAzE0SjPEhgXyyGabKlEEmVv3DjuvQIbnxaPtLARnk3_s-L7xw_fLj4vLq8-rS7OLxey6aq0wFRiVQsGEgvWkBrVulk3FWGKMdTUsquJRq2mTCgqKkw7pTHR0GGATpGGkrPi5cF3b33kUwUjx4SgtiWowplYHQjlxY7vg-lFuOFeGP53wYcNFzkvaYHnMCjTVdaiqhZEtUxpQFLQWiFUK8he76fThnUPSoJLuV4z0_mOM1u-8dec0A6TdgzmzWQQ_M8BYuK9iRKsFQ78MMaNWUurDjcZffUPen92E7UROQHjtM_nytGUn9esJV2NWpSp5T1U_tR4u_m5aZPXZ4K3M0FmEvxKGzHEyFdfv_w_e_Vjzr4-YrcgbNpGb4fxgcY5WB9AGXyMAfRdkTHiY7fcVoOP3cKnbsmyF8cXdCe6bQ_yB2CLEc4</recordid><startdate>20130307</startdate><enddate>20130307</enddate><creator>Ramani, R Geetha</creator><creator>Jacob, Shomona Gracia</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20130307</creationdate><title>Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models</title><author>Ramani, R Geetha ; Jacob, Shomona Gracia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-15c1d4a7ec1a763404f6b6237d77064c943f08f57ad5a2159df13fe91ee9d3653</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Analysis</topic><topic>Artificial intelligence</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Bioinformatics</topic><topic>Biology</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>Carcinoma, Non-Small-Cell Lung - classification</topic><topic>Classification</topic><topic>Cleaning</topic><topic>Cluster Analysis</topic><topic>Clustering</topic><topic>Computer applications</topic><topic>Computer Science</topic><topic>Data mining</topic><topic>Data Mining - methods</topic><topic>Data processing</topic><topic>Databases, Genetic</topic><topic>Diagnosis</topic><topic>Discriminant analysis</topic><topic>Divergence</topic><topic>DNA methylation</topic><topic>DNA microarrays</topic><topic>Drug development</topic><topic>Gene expression</topic><topic>Gene sequencing</topic><topic>Genes</topic><topic>Genetic counseling</topic><topic>Genomics</topic><topic>Health aspects</topic><topic>Humans</topic><topic>Hydrophobicity</topic><topic>Lung cancer</topic><topic>Lung diseases</topic><topic>Lung Neoplasms - classification</topic><topic>Mathematical models</topic><topic>Medical Informatics - methods</topic><topic>Medicine</topic><topic>Non-small cell lung cancer</topic><topic>Non-small cell lung carcinoma</topic><topic>Physicochemical properties</topic><topic>Polarizability</topic><topic>Properties (attributes)</topic><topic>Protein composition</topic><topic>Proteins</topic><topic>Proteins - chemistry</topic><topic>Reproducibility of Results</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ramani, R Geetha</creatorcontrib><creatorcontrib>Jacob, Shomona Gracia</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full 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>Ramani, R Geetha</au><au>Jacob, Shomona Gracia</au><au>Uversky, Vladimir N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2013-03-07</date><risdate>2013</risdate><volume>8</volume><issue>3</issue><spage>e58772</spage><epage>e58772</epage><pages>e58772-e58772</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Detecting divergence between oncogenic tumors plays a pivotal role in cancer diagnosis and therapy. This research work was focused on designing a computational strategy to predict the class of lung cancer tumors from the structural and physicochemical properties (1497 attributes) of protein sequences obtained from genes defined by microarray analysis. The proposed methodology involved the use of hybrid feature selection techniques (gain ratio and correlation based subset evaluators with Incremental Feature Selection) followed by Bayesian Network prediction to discriminate lung cancer tumors as Small Cell Lung Cancer (SCLC), Non-Small Cell Lung Cancer (NSCLC) and the COMMON classes. Moreover, this methodology eliminated the need for extensive data cleansing strategies on the protein properties and revealed the optimal and minimal set of features that contributed to lung cancer tumor classification with an improved accuracy compared to previous work. We also attempted to predict via supervised clustering the possible clusters in the lung tumor data. Our results revealed that supervised clustering algorithms exhibited poor performance in differentiating the lung tumor classes. Hybrid feature selection identified the distribution of solvent accessibility, polarizability and hydrophobicity as the highest ranked features with Incremental feature selection and Bayesian Network prediction generating the optimal Jack-knife cross validation accuracy of 87.6%. Precise categorization of oncogenic genes causing SCLC and NSCLC based on the structural and physicochemical properties of their protein sequences is expected to unravel the functionality of proteins that are essential in maintaining the genomic integrity of a cell and also act as an informative source for drug design, targeting essential protein properties and their composition that are found to exist in lung cancer tumors.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>23505559</pmid><doi>10.1371/journal.pone.0058772</doi><tpages>e58772</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2013-03, Vol.8 (3), p.e58772-e58772 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1330883021 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS) Journals Open Access; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Algorithms Analysis Artificial intelligence Bayes Theorem Bayesian analysis Bioinformatics Biology Biomarkers Cancer Carcinoma, Non-Small-Cell Lung - classification Classification Cleaning Cluster Analysis Clustering Computer applications Computer Science Data mining Data Mining - methods Data processing Databases, Genetic Diagnosis Discriminant analysis Divergence DNA methylation DNA microarrays Drug development Gene expression Gene sequencing Genes Genetic counseling Genomics Health aspects Humans Hydrophobicity Lung cancer Lung diseases Lung Neoplasms - classification Mathematical models Medical Informatics - methods Medicine Non-small cell lung cancer Non-small cell lung carcinoma Physicochemical properties Polarizability Properties (attributes) Protein composition Proteins Proteins - chemistry Reproducibility of Results Tumors |
| title | Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T09%3A59%3A59IST&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=Improved%20classification%20of%20lung%20cancer%20tumors%20based%20on%20structural%20and%20physicochemical%20properties%20of%20proteins%20using%20data%20mining%20models&rft.jtitle=PloS%20one&rft.au=Ramani,%20R%20Geetha&rft.date=2013-03-07&rft.volume=8&rft.issue=3&rft.spage=e58772&rft.epage=e58772&rft.pages=e58772-e58772&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0058772&rft_dat=%3Cgale_plos_%3EA478394080%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=1330883021&rft_id=info:pmid/23505559&rft_galeid=A478394080&rft_doaj_id=oai_doaj_org_article_7ec57f2330024a3d87dfe0ca54d004de&rfr_iscdi=true |