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How Machine Learning Helps Find Relevant Biomarkers in Cancer

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  • Fri, 02/02/2018 - 11:00am - 12:00pm




How Machine Learning Helps Find Relevant Biomarkers in Cancer

Computer Science Colloquium Series
Dr. Luis Rueda

Date:  Friday, February 2nd, 2018
Time: 11:00 am
Location: Odette, 110

Abstract: Machine learning, a field of artificial intelligence, has been successfully used for data analysis in many different applications, including transcriptomics, cancer research, life science, finance, and many others. Biotechnological tools for next generation sequencing has produced very large datasets which are difficult to analyze without the help of specialized learning algorithms. However, knowledge discovery from next generation sequencing data may not be meaningful if not targeting the right biological questions. In contrast, life sciences need the support from those algorithms to extract a few meaningful biomarkers from thousands of them. In this regard, multi-disciplinary collaborative research efforts play an important role, in which participants bring in their knowledge in a cohesive manner, yielding a more effective approach. In this talk, machine learning approaches for finding relevant transcripts and potential proteins associated with cancer diagnosis, prognosis and treatment will be discussed. Some results and interpretation of biomarkers in prostate cancer progression and scoring will be presented.

Bio: Luis Rueda received his Bachelor’s degree in computer science from the National University of San Juan, Argentina, in 1993, and his Master’s and Ph.D. degrees in computer science from Carleton University in 1998 and 2002, respectively. He is currently a Full Professor within the School of Computer Science at the University of Windsor. His research interests are mainly focused on theoretical and applied machine learning and pattern recognition, mostly in the fields of transcriptomics, interactomics and genomics with applications to cancer research. He holds three patents on data encryption and has published more than 120 publications in prestigious journals and conferences in machine learning and bioinformatics. He is a Senior Member of the IEEE, and a Member of the Association for Computing Machinery and the International Society for Computational Biology.

 



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