Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models.

ABSTRACT: Rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important in diagnosis of this disease. Furthermore sequence-derived structural and physicochemical descriptors are very useful for machine learning prediction of protein structural and functional classes, classifying proteins and the prediction performance. Herein, in this study is the classification of lung tumors based on 1497 attributes derived from structural and physicochemical properties of protein sequences (based on genes defined by microarray analysis) investigated through a combination of attribute weighting, supervised and unsupervised clustering algorithms. Eighty percent of the weighting methods selected features such as autocorrelation, dipeptide composition and dist

SUBMITTER: Hosseinzadeh F 

PROVIDER: S-EPMC3400626 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature