ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2013
Detection of protein catalytic sites in the biomedical literature.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- PDF text classification to leverage information extraction from publication reports.Journal of biomedical informatics · 2016Article
- Literature mining of genetic variants for curation: quantifying the importance of supplementary material.Database : the journal of biological databases and curation · 2014Article
- Mutation extraction tools can be combined for robust recognition of genetic variants in the literature.F1000Research · 2014Article
- Associating disease-related genetic variants in intergenic regions to the genes they impact.PeerJ · 2014Article
- Annotating the biomedical literature for the human variome.Database : the journal of biological databases and curation · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This paper explores the application of text mining to the problem of detecting protein functional sites in the biomedical literature, and specifically considers the task of identifying catalytic sites in that literature. We provide strong evidence for the need for text mining techniques that address residue-level protein function annotation through an analysis of two corpora in terms of their coverage of curated data sources. We also explore the viability of building a text-based classifier for identifying protein functional sites, identifying the low coverage of curated data sources and the potential ambiguity of information about protein functional sites as challenges that must be addressed. Nevertheless we produce a simple classifier that achieves a reasonable ∼69% F-score on our full text silver corpus on the first attempt to address this classification task. The work has application in computational prediction of the functional significance of protein sites as well as in curation workflows for databases that capture this information.
Indexed as
Identifiers
23424147PMC3664919What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.