Identifying biomarkers that can be used to classify certain disease stages or identify when a disease becomes more aggressive is one of the most important applications of machine learning. Traditional biomarker identification approaches, typically, use machine learning techniques to identify a number of genes and macromolecules as biomarkers that can be used to diagnose specific diseases or states of diseases with very high accuracy, using molecular measurements such as mutations, gene expression, copy number variations, and others. However, experts' opinions and knowledge is required to validate such findings. We propose a new machine learning model that incorporates a knowledge-assisted system used to integrate the findings of the DisGeNET database, which is a framework that provides proven relationships among diseases and genes. The machine learning pipeline starts by reducing the number of features using a filter-based feature selection method. The DisGeNET database is used to score each gene relating to the given cancer name. Then a wrapper-based feature-selection algorithm picks the best set of genes with the highest classification accuracy. The method has been able to retrieve key genes from multiple data sets that classify with very high accuracy while being biologically relevant, and no human intervention needed. Initial results provide a high area-under-the-curve with a handful of genes that are already proven to be related to the relevant disease and state based on the latest published medical findings.

 

 

Osama Hamzeh is a Ph.D. candidate and research assistant in the school of computer science at the University of Windsor, working under the supervision of Prof. Luis Rueda. Osama’s research interests include A.I., machine learning, bioinformatics, text mining, RNA sequencing and cancer progression. He has multiple journal articles and research papers in some of the most prestigious conferences in bioinformatics. This includes research on Prostate cancer Gleason score’s prediction, and tumor location classification based on gene expressions for prostate cancer patients.

 

 

 

Date:  Friday December 6, 2019

 

Time: 11:00am

 

Location: Essex Hall Room 122

 

 

 

For Information:

 

Dr. Sherif Saad Ahmed

 

 

5113 Lambton Tower, 401 Sunset Ave., Windsor ON, N9B 3P4 (519) 253-3000 Ext. 3716 csgradinfo@uwindsor.ca