T-PAIR: Temporal node-pair embedding for automatic biomedical hypothesis generation

Uchenna Akujuobi

Michael Spranger

Sucheendra K Palaniappan*

Xiangliang Zhang*

* External authors

IEEE Transactions on Knowledge and Data Engineering

2020

Abstract

In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningful
implicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted from
databases of biomedical publications. Most prior studies of this problem focused on the use of static information of terms and largely
ignored the temporal dynamics of scientific term relations. Even when the dynamics were considered in a few recent studies, they
learned the representations for the scientific terms, rather than focusing on the term-pair relations. Since the HG problem is to predict
term-pair connections, it is not enough to know with whom the terms are connected, it is more important to know how the connections
have been formed (in a dynamic process). We formulate this HG problem as a future connectivity prediction in a dynamic attributed
graph. The key is to capture the temporal evolution of node-pair (term-pair) relations. We propose an inductive edge (node-pair)
embedding method named T-PAIR, utilizing both the graphical structure and node attribute to encode the temporal node-pair
relationship. We demonstrate the efficiency of the proposed model on three real-world datasets, which are three graphs constructed
from Pubmed papers published until 2019 in Neurology, Immunotherapy, and Virology, respectively. Evaluations were conducted on
predicting future term-pair relations between millions of seen terms (in the transductive setting), as well as on the relations involving
unseen terms (in the inductive setting). Experiment results and case study analyses show the effectiveness of the proposed model.