Learning to Rank Complex Biomedical Hypotheses for Accelerating Scientific Discovery.

Abstract

Hypothesis generation (HG) is a fundamental problem in biomedical text mining that uncovers plausible implicit links ( $B$ terms) between two disjoint concepts of interest ( $A$ and $C$ terms). Over the past decade, many HG approaches based on distributional statistics, graph-theoretic measures, and supervised machine learning methods have been proposed. Despite significant advances made, the existing approaches have two major limitations. First, they mainly focus on enumerating hypotheses and often neglect to rank them in a semantically meaningful way. This leads to wasted time and resources as researchers may focus on hypotheses that are ultimately not supported by experimental evidence. Second, the existing approaches are designed to rank hypotheses with only one intermediate or evidence term (referred as simple hypotheses), and thus are unable to handle hypotheses with multiple intermediate terms (referred as complex hypotheses). This is limiting because recent research has shown that the complex hypotheses could be of greater practical value than simple ones, especially in the early stages of scientific discovery. To address these issues, we propose a new HG ranking approach that leverages upon the expressive power of Graph Neural Networks (GNN) coupled with a domain-knowledge guided Noise-Contrastive Estimation (NCE) strategy to effectively rank both simple and complex biomedical hypotheses. Specifically, the message passing capabilities of GNN allows our approach to capture the rich interactions between biomedical entities and succinctly handle the complex hypotheses with variable intermediate terms. Moreover, the proposed domain knowledge-guided NCE strategy enables the ranking of complex hypotheses based on their coherence with the established biomedical knowledge. Extensive experiment results on five recognized biomedical datasets show that the proposed approach consistently outperforms the existing baselines and prioritizes hypotheses worthy of potential clinical trials.