Authors

* External authors

Venue

Date

Share

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.

Related Publications

Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning

Nature, 2022
Pete Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J. Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, Leilani Gilpin, Piyush Khandelwal, Varun Kompella, Hao Chih Lin, Patrick MacAlpine, Declan Oller, Takuma Seno, Craig Sherstan, Michael D. Thomure, Houmehr Aghabozorgi, Leon Barrett, Rory Douglas, Dion Whitehead Amago, Peter Dürr, Peter Stone, Michael Spranger, Hiroaki Kitano

Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block…

Logic Tensor Networks

Artificial Intelligence, 2022
Samy Badreddine, Artur d'Avila Garcez*, Luciano Serafini*, Michael Spranger

Attempts at combining logic and neural networks into neurosymbolic approaches have been on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists deep learning, which typically uses a sub-symbolic distributed representation, to learn and reason a…

Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation

NeurIPS, 2021
Ryuji Imamura, Takuma Seno, Kenta Kawamoto, Michael Spranger

When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art realistic racing game AI agent that outperforms human players does not use image-based environ…

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

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.