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Machine Learning in Cancer and Health Informatics

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  • Fri, 03/02/2018 - 2:00pm - 3:00pm

Cancer is the leading cause of death in Canada, responsible for nearly 30% of all deaths, the advances in technology help in analyzing the disease. More recently, Next-generation sequencing technology (NGS) has emerged, decreasing the cost and increasing the speed of genome and transcriptome sequencing. However, sequences come with artifacts, and hence preprocessing the reads is required for downstream analysis. The analysis can determine which genes and transcripts are relevant for different cases, which can provide some biological insight for each case, leading to improved diagnosing and treatment of different diseases including cancer based on those transcripts and protein products.


On the other hand, machine learning methods have been used to classify different conditions such as survivability and subtypes in breast cancer, and stages and location in prostate cancer. Using supervised learning techniques, transcript expression profiles as features or biomarkers have been found to be key indicators for different conditions. Similarly, unsupervised methods have been used to group similar trends of profiles throughout different cancer stages. In this talk, how these methods are used to analyze prostate cancer progression throughout different stages will be discussed. The main purpose is to find genes, transcripts and protein isoforms that have similar trends, suppressors, motives, and promoters. Clustering approaches used to find patterns in significantly expressed transcripts among the different cancer stages will also be discussed.


Abed Alkhateeb is a Ph.D. candidate in the Pattern Recognition and Bioinformatics lab in the School of Computer Science. He completed his Bachelor in the department of Computer Science at University of Jordan, and his M.Sc. in computer science in the School of Computer Science at the University of Windsor. He has had positions in Blackberry as Business Systems Developer, and before doing his Ph.D. as Analyst/Programmer in Emara-tech and UAE University in the United Arab Emirates. Abed’s research interests include machine learning, bioinformatics, health informatics, pharmacogenomics, and RNA sequencing and cancer progression. He has many publications and presentations in most top-ranked conferences in bioinformatics and reputable journals. Abed has recently obtained the Ontario Government Scholarship and the Faculty of Science Going Above and Beyond award, among other awards.

Margaret Garabon
(519)253-3000 ext.3714