Seminars Details


Virtual Seminar: Computational Drug Repositioning Based on Integrated Similarity Measures and Deep Learning

Tamer N. Jarada; Postdoctoral Associate in Real-World Evidence Analytics in the Department of Oncology at the University of Calgary

ABSTRACT: Drug repositioning is an emerging pharmaceutical research approach for identifying novel therapeutic potentials for approved drugs and discovering therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional de novo drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches.
Numerous attempts have been carried out, with different degrees of efficiency and success, to computationally study the potential of identifying alternative drug indications, which slow, stop, or reverse the courses of incurable diseases.More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made computational drug repositioning an area of intense activities. In this talk, integrating various biological and biomedical data from different sources to improve the quality of biomedical knowledge in the computational drug repositioning field is addressed. The main contribution of this research is two-fold. First, it proposes a robust framework that utilizes known drug-disease interactions and drug-related similarity information to predict new drug-disease interactions. Second, it introduces a novel integrative framework for predicting drug-disease interactions using known drug-disease interactions, drug-related similarity information, and disease-related similarity information. The two proposed frameworks leverage advanced similarity calculation, selection, and integration to understand the functional and behavioural correlation between drugs and diseases. Furthermore, they employ the most advanced machine learning tools in predicting hidden or indirect drug-disease interactions for potential drug repositioning applications.

BIO: Tamer N. Jarada is a Data Scientist who holds a Ph.D. in Computer Science from the University of Calgary with over ten years of academic and professional experience. He is currently a Postdoctoral Associate in Real-World Evidence Analytics in the Department of Oncology at the University of Calgary. Tamer received his M.Sc. in Computer Science and B.Sc. in Computer Engineering from the University of Calgary and IU Gaza. In his Ph.D. research, he addressed the demand for integrating various biological and biomedical datasets for data-driven insights in the Computational Drug Repositioning field. Tamer’s primary work and research interests are Health Informatics & Analytics, Bioinformatics, Biomedicine, Computational Biology, Machine Learning, Network Analysis, Natural Language Processing, Big Data Analytics, and Data Visualization. His track record consists of more than 25 papers in refereed international journals and conferences. Tamer has taught and provided instructional training to students from different disciplines in more than a dozen courses. He also developed courses for two recent professional Data Science and Software Engineering programs introduced by the Computer Science and Electrical & Computer Engineering Departments at the University of Calgary.