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Prediction of cis-Regulatory Genomic Elements using Machine Learning Approaches

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  • Fri, 10/06/2017 - 11:00am - 12:00pm

Prediction of cis-Regulatory Genomic Elements using Machine Learning Approaches

Computer Science Colloquium Series
Dr. Alioune Ngom

Date:  Friday, October 6th, 2017
Time: 11:00 am
Location: Chrysler Hall North, G100

Abstract: Identifying active cis-regulatory genomic elements (CREs) in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we use and compare six machine learning (ML) methods (provided in the WEKA machine learning environment) for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, ML methods can enable a significant advance in and from our knowledge of the genomic locations of CREs. Using ten-fold cross-validation to build robust classifier models, we applied the six ML methods on multiple cell-line data and then reported the performances of all models over all and in all cell lines. Traditional biological methods to identify cis-regulatory genomic elements from transcriptional data can be time-consuming. Complete network mapping is often a problem far too complex to solve as a result of the inherent limitations of these methods. Computational approaches can help facilitate the process by using validated data to train models in efficiently discovering novel interactions. Many studies employ Support Vector Machines as the algorithm of choice, however we found that learning algorithms such as Random Forest methods consistently perform better in cis-regulatory element classification. This result is carried over multiple human cell lines and against other various algorithms.

Bio: Professor Alioune Ngom recieved his BSc in computer science from the Universite du Quebec a Trois-Rivieres, and his MSc and PhD in computer science from the University of Ottawa. He held an Assistant Professor position in computer science at Lakehead University. He is currently a Full Professor within the School of Computer Science at the University of Windsor. His main research interests currently focus on  (1) the study of network-based machine learning algorithms and their applications to big data analysis, bioinformatics, and cancer bioinformatics, and (2) the study and applications of sparse representation learning methods. Dr. Ngom holds one patent and has published more than 120 publications in journals and conference proceedings, in the areas of machine learning and bioinformatics. He is a member of the International Society for Computational Biology.

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