Prediction of lung tumor types based on protein attributes by machine learning algorithms.

ABSTRACT: Early diagnosis of lung cancers and distinction between the tumor types (Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are very important to increase the survival rate of patients. Herein, we propose a diagnostic system based on sequence-derived structural and physicochemical attributes of proteins that involved in both types of tumors via feature extraction, feature selection and prediction models. 1497 proteins attributes computed and important features selected by 12 attribute weighting models and finally machine learning models consist of seven SVM models, three ANN models and two NB models applied on original database and newly created ones from attribute weighting models; models accuracies calculated through 10-fold cross and wrapper validation (just for SVM al

SUBMITTER: Hosseinzadeh F 

PROVIDER: S-EPMC3710575 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature