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Keynote

Combining Evolutionary Algorithms and Machine Learning in Bioinformatics

Amarda Shehu

George Mason University

Abstract: This talk will provide an overview of recent computational methods that combine evolutionary algorithms with machine learning techniques for annotation of biological sequences and structures. Many problems of interest in bioinformatics necessitate annotation of biological molecules with structural or functional information. For instance, determining that a protein is not crystallizable from its sequence can save futile attempts to determine the structure through X-ray crystallography. Yet other problems involve predicting splice sites and regulatory regions in DNA molecules. Annotation often involves extracting descriptive features, a task that has commonly been the domain of problem experts or a combination of painstaking efforts and luck. Recent research advocates employing evolutionary algorithms for the automatic discovery of meaningful biological features. This talk will provide an overview of a general emerging framework that combines evolutionary algorithms with machine learning techniques and showcase several successful applications of this framework in bioinformatics.

Bio: Dr. Amarda Shehu is an Assistant Professor in the Department of Computer Science at George Mason University. She holds affiliated appointments in the Department of Bioinformatics and Computational Biology and the Bioengineering Program at George Mason University. She received her Ph.D. in Computer Science from Rice University in Houston, TX in 2008, where she was also an NIH fellow of the Nanobiology Training Program of the Gulf Coast Consortia. Her research in computational biophysics and bioinformatics focuses on advancing understanding of the sequence-structure-function relationship in biological molecules. Her work combines probabilistic search frameworks with the theory of statistical mechanics and evolutionary algorithms with machine learning.

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