Identification of marker genes in Alzheimer's disease using a machine-learning model.

Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis.

A comprehensive evaluation of machine learning techniques for cancer class prediction based on microarray data.

Prostate cancer is among the most common cancer in males and its heterogeneity is well known. The genomic level changes can be detected in gene expression data and those changes may serve as standard model for any random cancer data for class prediction. Various techniques were implied on prostate cancer data set in order to accurately predict cancer class including machine learning techniques.

An in silico analytical study of lung cancer and smokers datasets from gene expression omnibus (GEO) for prediction of differentially expressed genes.

Smoking is the leading cause of lung cancer development and several genes have been identified as potential biomarker for lungs cancer. Contributing to the present scientific knowledge of biomarkers for lung cancer two different data sets, i.e.