Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature

Autor:	K��ksal, Abdullatif, D��nmez, Hilal, ��z��elik, R��za, Ozkirimli, Elif, ��zg��r, Arzucan
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Molecular Networks (q-bio.MN) FOS: Biological sciences Quantitative Biology - Molecular Networks Information Retrieval (cs.IR) Machine Learning (cs.LG) Computer Science - Information Retrieval
Popis:	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/ . EMNLP 2020 - COVID-19 Workshop
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24031739111378c9b45d4b04e902de30 http://arxiv.org/abs/2009.02526 Zobrazit plný text záznamu