Assoc. Prof. Tunca Doğan will give a seminar on “Development and Application of Data-Driven Approaches to Make Sense out of Large-Scale and Heterogeneous Biomolecular Data” on 15 November at 13:40. The abstract of the talk and a short bio is shared below.

Bio:
Dr. Tunca Doğan has graduated from both the undergraduate and MSc programs in Middle East Technical University (METU), Faculty of Engineering, studying in the field of process engineering. He started conducting research in computational biology and bioinformatics areas during his Ph.D. study back in 2010. He received his joint Ph.D. degree from the interdisciplinary Bioengineering program, hosted by the Electrical & Electronics Engineering Department in Izmir Institute of Technology, and the Graduate School of Health Sciences and the Faculty of Medicine, DEU in 2013. Between the years 2013 and 2016, Dr. Dogan served as a post-doctoral researcher at the European Bioinformatics Institute (EMBL-EBI), Protein Function Development (UniProt) team, and at the University of Cambridge, UK. Between 2016 and 2019, he worked both as a research associate at EMBL-EBI and as an adjunct faculty member at the Institute of Informatics in METU, Turkey. Dr. Dogan currently is a faculty member at the Department of Computer Engineering, Hacettepe University, Turkey. His research interests lie in the fields of bioinformatics and cheminformatics, and can be summarized as developing and applying data science and AI-based computational methods for biomolecular function/property prediction, and computational drug discovery.

Abstract:
The recent availability of inexpensive technologies led to a surge of biological/biomedical data production and accumulation in public servers. These noisy, complex and large-scale data should be analyzed in order to understand mechanisms that constitute life and to develop new and effective treatments against prevalent diseases. A key concept in this endeavour is the prediction of unknown attributes and properties of biomolecules (i.e., genes, proteins and RNAs) such as their molecular functions, physical interactions, etc., together with their relationships to high-level biomedical concepts such as systems and diseases. Lately, cutting-edge data-driven approaches are starting to be applied to biological data to aid the development of novel and effective in silico solutions. In this seminar, I’ll summarize our efforts for integrating and representing heterogeneous data from different biological/biomedical data resources (i.e., the CROssBAR project) together with the development and application of deep learning-based computational methods for enriching the integrated data by predicting unknown functions and drug discovery centric ligand interactions of human genes and proteins.