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...

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Autores principales:	R Geetha Ramani, Shomona Gracia Jacob
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7ec57f2330024a3d87dfe0ca54d004de
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spelling	oai:doaj.org-article:7ec57f2330024a3d87dfe0ca54d004de2021-11-18T07:54:18ZImproved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.1932-620310.1371/journal.pone.0058772https://doaj.org/article/7ec57f2330024a3d87dfe0ca54d004de2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23505559/?tool=EBIhttps://doaj.org/toc/1932-6203Detecting 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.R Geetha RamaniShomona Gracia JacobPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 3, p e58772 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q R Geetha Ramani Shomona Gracia Jacob Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.
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.
format	article
author	R Geetha Ramani Shomona Gracia Jacob
author_facet	R Geetha Ramani Shomona Gracia Jacob
author_sort	R Geetha Ramani
title	Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.
title_short	Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.
title_full	Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.
title_fullStr	Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.
title_full_unstemmed	Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.
title_sort	improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/7ec57f2330024a3d87dfe0ca54d004de
work_keys_str_mv	AT rgeetharamani improvedclassificationoflungcancertumorsbasedonstructuralandphysicochemicalpropertiesofproteinsusingdataminingmodels AT shomonagraciajacob improvedclassificationoflungcancertumorsbasedonstructuralandphysicochemicalpropertiesofproteinsusingdataminingmodels
_version_	1718422787307077632

Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.

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