Interpretable Visualization of Scientific Hypotheses in Literature-based Discovery
Tyagin, I.; Safro, I.
Show abstract
In 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.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Datavzrd: Rapid programming- and maintenance-free interactive visualization and communication of tabular data 94%
- Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions 94%
- Academic Tracker: Software for Tracking and Reporting Publications Associated with Authors and Grants 94%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.