Analysis for Disease Gene Association Using Machine Learning

To recognize the basis of disease, it is essential to determine its underlying genes. Understanding the association between underlying genes and genetic disease. This is a fundamental problem regarding human health. Identification and association of genes with the disease require time consuming and expensive experimentations of a great number of potential candidate genes. 

Therefore, the alternative inexpensive and rapid computational methods have been proposed that can identify the candidate gene associated with a disease. Most of these methods use phenotypic similarities due to the fact that genes causing same or similar diseases have less variation in their sequence or network properties of proteinprotein interactions based on-premises that genes lie closer in protein interaction network that causes the similar or same disease.

 In this project, we propose and analyze a novel computational methods for the identification of genes associated with diseases. Some advance topological and biological features that are overlooked currently are introducing for identifying candidate genes. We evaluate different computational methods on disease-gene association data from DisGeNET in a 10-fold cross-validation mode based on TP rate, FP rate, precision, recall, F-measure, and ROC curve evaluation parameters.

Keywords: Disease Gene Association, Protein-Protein Interaction Network (PPIN), Electron-Ion Interaction Pseudopotential (EIIP).