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Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Uchenna Akujuobi · Jun Chen · Mohamed Elhoseiny · Michael Spranger · Xiangliang Zhang

Mon Dec 07 09:00 PM -- 11:00 PM (PST) @ Poster Session 0 #22

Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases. Many attempts have been made to introduce the use of machine learning to the scientific process of hypothesis generation (HG), which refers to the discovery of meaningful implicit connections between biomedical terms. However, most existing methods fail to truly capture the temporal dynamics of scientific term relations and also assume unobserved connections to be irrelevant (i.e., in a positive-negative (PN) learning setting). To break these limits, we formulate this HG problem as future connectivity prediction task on a dynamic attributed graph via positive-unlabeled (PU) learning. Then, the key is to capture the temporal evolution of node pair (term pair) relations from just the positive and unlabeled data. We propose a variational inference model to estimate the positive prior, and incorporate it in the learning of node pair embeddings, which are then used for link prediction. Experiment results on real-world biomedical term relationship datasets and case study analyses on a COVID-19 dataset validate the effectiveness of the proposed model.

Author Information

Uchenna Akujuobi (KAUST and Sony AI)
Jun Chen (King Abdullah University of Science and Techonology)
Mohamed Elhoseiny (KAUST and Stanford University)
Michael Spranger (Sony)
Xiangliang Zhang (" King Abdullah University of Science and Technology, Saudi Arabia")

Dr. Xiangliang Zhang is an Associate Professor of Computer Science and directs the MINE (http://mine.kaust.edu.sa) group at KAUST, Saudi Arabia. She earned her Ph.D. degree in computer science from INRIA-University Paris-Sud, France, in July 2010. She received her M.S. and B.S. degrees from Xi’an Jiaotong University, China, in 2006 and 2003, respectively. Dr. Zhang's research mainly focuses on learning from complex and large-scale streaming data and graph data, with applications on recommendation systems, biomedical knowledge discovery and social media data analysis. Dr. Zhang has published over 160 research papers in referred international journals and conference proceedings, including TKDE, SIGKDD, AAAI, IJCAI, NeurIPS, ICDM, etc. She regularly serves on the Program Committee for premier conferences like SIGKDD (Senior PC), AAAI (Senior PC), IJCAI (Area Chair, Senior PC), ICDM, NIPS, ICML etc. Dr. Zhang was invited to deliver an Early Career Spotlight talk at IJCAI-ECAI 2018.

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