Abstract
ABSTRACTIn this paper we present an approach for interpretable visualization of scientific hypotheses that is based on the idea of semantic concept interconnectivity, network-based and topic modeling methods. Our visualization approach has numerous adjustable parameters which provides the domain experts with additional flexibility in their decision making process. We also make use of the Unified Medical Language System metadata by integrating it directly into the resulting topics, and adding the variability into hypotheses resolution. To demonstrate the proposed approach in action, we deployed end-to-end hypothesis generation pipeline AGATHA, which was evaluated by BioCreative VII experts with COVID-19-related queries.
Publisher
Cold Spring Harbor Laboratory
Reference11 articles.
1. Justin Sybrandt , Michael Shtutman , and Ilya Safro . Moliere: Automatic biomedical hypothesis generation system. In Proceedings of the 23rd ACM SIGKDD, KDD ‘17, pages 1633–1642, New York, NY, USA, 2017. ACM.
2. Justin Sybrandt , Ilya Tyagin , Michael Shtutman , and Ilya Safro . AGATHA: Automatic Graph Mining And Transformer Based Hypothesis Generation Approach, page 2757–2764. Association for Computing Machinery, New York, NY, USA, 2020.
3. Justin Sybrandt , Angelo Carrabba , Alexander Herzog , and Ilya Safro . Are abstracts enough for hypothesis generation? In 2018 IEEE International Conference on Big Data (Big Data), pages 1504–1513. IEEE, 2018.
4. Ilya Tyagin , Ankit Kulshrestha , Justin Sybrandt , Krish Matta , Michael Shtutman , and Ilya Safro . Accelerating covid-19 research with graph mining and transformer-based learning, 2021.
5. Adam Lerer , Ledell Wu , Jiajun Shen , Timothee Lacroix , Luca Wehrstedt , Abhijit Bose , and Alex Peysakhovich . PyTorch-BigGraph: A Large-scale Graph Embed-ding System. In Proceedings of the 2nd SysML Conference, Palo Alto, CA, USA, 2019.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献