Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature
Not on the map. Why?
Abstract (arXiv)
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/ .
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.
3 papers.
- 2022BERN2: an advanced neural biomedical named entity recognition and normalization toolcited by 42
- 2024Augmenting Biomedical Named Entity Recognition with General-domain Resourcescited by 2
- 2021Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extractioncited by 0
- 2024Augmenting Biomedical Named Entity Recognition with General-domain Resources15 Jun 2024
- 2022BERN2: an advanced neural biomedical named entity recognition and normalization tool6 Jan 2022
- 2021Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction15 Jun 2021