ProPheno: An online dataset for completely characterizing the human protein-phenotype landscape in biomedical literature
- Published
- Accepted
- Subject Areas
- Bioinformatics, Natural Language and Speech
- Keywords
- ProPheno, Biomedical Natural Language Processing, Proteins/Phenotypes, Text Mining
- Copyright
- © 2019 Pourreza Shahri et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. ProPheno: An online dataset for completely characterizing the human protein-phenotype landscape in biomedical literature. PeerJ Preprints 7:e27479v1 https://doi.org/10.7287/peerj.preprints.27479v1
Abstract
Identifying protein-phenotype relations is of paramount importance for applications such as uncovering rare and complex diseases. One of the best resources that captures the protein-phenotype relationships is the biomedical literature. In this work, we introduce ProPheno, a comprehensive online dataset composed of human protein/phenotype mentions extracted from the complete corpora of Medline and PubMed. Moreover, it includes co-occurrences of protein-phenotype pairs within different spans of text such as sentences and paragraphs. We use ProPheno for completely characterizing the human protein-phenotype landscape in biomedical literature. ProPheno, the reported findings and the gained insight has implications for (1) biocurators for expediting their curation efforts, (2) researches for quickly finding relevant articles, and (3) text mining tool developers for training their predictive models. The RESTful API of ProPheno is freely available at http://propheno.cs.montana.edu.
Author Comment
This is a submission to PeerJ Computer Science for review.