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Talks & Seminars
Title: Contextually Sensitive and Semantically Interpretative Literature Based Discovery from Biomedical Text
Dr. Vishrawas Gopalakrishnan, IBM Watson Health
Date & Time: November 26, 2018 11:30
Venue: Conference Room, 01st Floor, CSE KR Bldg.
Abstract:
The overwhelming amount of research articles in the domain of bio-medicine leads to important connections being unnoticed. Literature Based Discovery is a sub-field within biomedical text mining that peruses these articles to formulate high confident hypotheses on possible connections between medical concepts. Although many alternate methodologies have been proposed over the last decade, they still suffer from scalability issues. The primary reason, apart from the dense inter-connections between biological concepts, is the absence of information on the factors that lead to edge-formation. Another limitation that many methodologies share is the lack of inherent temporal modeling. In this talk, I will focus on two aspects of this problem - efficiency and effectiveness. In the first part, I will talk on how formulating this problem as a collaborative filtering task with word-vectors helps us learn and mimic the implicit edge-formation process; thus, allowing us to prune the search-space of redundant and irrelevant hypotheses. The second part dwells on 'Time-Aware Top-k Conceptual Bridges' that extends the efficiency module by incorporating temporal aspects to the word-vectors. These temporally sensitive word-vectors allow the system to track changes in the associations between medical concepts and focus on a pair of concepts that have the highest 'towards velocity'.
Speaker Profile:
Dr. Vishrawas Gopalakrishnan is a Data Scientist in the 'Analytics Center of Excellence' group in IBM Watson Health. His primary research interest is in text mining, and his thesis focused on developing unsupervised and contextually sensitive algorithms for both core tasks in a text mining pipeline as well as in application-related settings. He has actively participated in other research topics as well, with topics ranging from multi-source learning in biological networks to classical NLP and IR problems like extraction of multi-word expressions in transliterated texts. At IBM Watson Health, he is responsible for NLP and semantics related projects within his team and closely works with IBM Research in maturing research assets and innovating commercial products. Vishrawas graduated in 2017 from State University of New York at Buffalo where he worked under the tutelage of Prof. Aidong Zhang. He has published in top-tier conferences and journals like VLDB, CIKM, KDD, ICDM, Bioinformatics and TKDE. He serves as a reviewer for many of these conferences and journals and was a PC member for this year’s KDD Workshop on Machine Learning for Medicine and Healthcare.
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