Authors
- Uchenna Akujuobi
- Michael Spranger
- Sucheendra Palaniappan*
- Xiangliang Zhang*
* External authors
Venue
- ICDE 2023
Date
- 2023
T50: T-PAIR: Temporal Node-pair Embedding for Automatic Biomedical Hypothesis Generation (Extended abstract)
Sucheendra Palaniappan*
Xiangliang Zhang*
* External authors
ICDE 2023
2023
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 using 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 and 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 real-world biomedical datasets in 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).
Related Publications
As scaling laws in generative AI push performance, they simultaneously concentrate the development of these models among actors with large computational resources. With a focus on text-to-image (T2I) generative models, we aim to unlock this bottleneck by demonstrating very l…
While existing vision and multi-modal foundation models can handle multiple computer vision tasks, they often suffer from significant limitations, including huge demand for data and computational resources during training and inconsistent performance across vision tasks at d…
In the ever-evolving domain of food computing, named entity recognition (NER) presents transformative potential that extends far beyond mere word tagging in recipes. Its implications encompass intelligent recipe recommendations, health analysis, and personalization. Neverthe…
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.