This paper proposes a Temporal Attention Networks (TAN) to produce powerful spatiotemporal embeddings for Biomedical Hypothesis Generation.

Methods. This paper proposes a Temporal Attention Networks (TAN) to produce powerful spatiotemporal embeddings for Biomedical Hypothesis Generation.

Methods. This paper proposes a Temporal Attention Networks (TAN) to produce powerful spatiotemporal embeddings for Biomedical Hypothesis Generation.

Journal: Journal of Biomedical Informatics, 2024, p. 104607 ; Publisher: Elsevier BV ; Authors: Huiwei Zhou, Haibin Jiang, Lanlan Wang, Weihong Yao, Yingyu Lin.

Oct 4, 2022 · This article proposes a novel temporal difference embedding (TDE) learning framework to model the temporal difference information evolution of term-pair ...

Weihong Yao's 9 research works with 57 citations and 652 reads, including: Generating Biomedical Hypothesis with Spatiotemporal Transformers.

Temporal attention networks for biomedical hypothesis generation Accepted by The Journal of Biomedical informatics. link. Data. The link of all three datasets ...

Our experimental results demonstrate that by employing incremental training with THiGER-A, we achieve enhanced convergence and performance for hypothesis- ...

Hypothesis generation (HG) refers to the task of mining meaningful implicit association between unlinked biomedical concepts. The majority of prior studies ...

Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases.