Computer Science Colloquium Series
Dr. Luis Rueda
Date: Friday, February 17th, 2017
Time: 11:00 am
Location: Chrysler Hall North, G100
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 extraction from 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 an effective approach to life sciences. In this talk, machine learning approaches for finding relevant transcripts and potential proteins associated with cancer progression will be discussed. A general collaborative scheme will be shown, as well as some results in prostate cancer progression 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. He holds one patent on data encryption and has published more than 100 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