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Finding Transcripts Associated with Prostate Cancer Gleason Stages using Next Generation Sequencing and Machine Learning Techniques

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  • Fri, 10/20/2017 - 11:00am - 12:00pm

Finding Transcripts Associated with Prostate Cancer Gleason Stages using Next Generation Sequencing and Machine Learning Techniques

Computer Science Colloquium Series
Osama Hamzeh

Date:  Friday, October 20th, 2017
Time: 11:00 am
Location: Chrysler Hall North, G100

Abstract: Prostate cancer is a leading cause of death world-widely and the third leading cause of cancer death for men in North America. Prostate cancer causes parts of the prostate cells to lose normal control of growth and division. The Gleason classification system is one of the known methods used to grade the aggressiveness of the prostate progression. In this study, an RNA-Seq dataset of 104 prostate cancer patients with different Gleason stages is analyzed using machine learning techniques to identify transcripts linked to prostate progression.

The proposed method utilizes information gain as a ranker for feature selection to overcome the curse of dimensionality. Minimum Redundancy Maximum Relevance (MRMR) feature selection was applied on a one-versus-all hierarchical classification model to find the best subset of transcripts that predicts each stage. The Naive Bayes classifier was used at each node of the hierarchical model. Naive Bayes is compared with support vector machine (SVM) for accuracy as a performance measure. Several transcripts are found to be highly associated with different Gleason stages in prostate cancer patients. 

Bio: Osama Hamzeh is a Ph.D. student in the Pattern Recognition and Bioinformatics lab in the School of Computer Science, working under the supervision of Dr. Luis Rueda. He completed his Bachelor in the department of Computer Science at Ajman University, UAE, and his M.Sc. in computer science in the School of Computer Science at the University of Sharjah, UAE. Before doing his PhD he worked in multiple IT fields for more than 16 years in security and system administration in University of Sharjah in United Arab Emirates. Osama’s research interests include A.I., machine learning, bioinformatics, and RNA sequencing and cancer progression. He has papers and posters publications in some of the most top-ranked conferences in bioinformatics. Osama’s masters degree thesis was about robotics and autonomous systems using probabilistic approaches.

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