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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Veröffentlicht in:	PloS one 2013-03, Vol.8 (3), p.e58772-e58772
Hauptverfasser:	Ramani, R Geetha, Jacob, Shomona Gracia
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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