ArticleGenome biology2008
Text mining for biology--the way forward: opinions from leading scientists.
Article in Genome biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 101 citations in OpenAlex.
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- DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.PLoS computational biology · 2023Article
- Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook.Briefings in bioinformatics · 2021Article
- Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network.Scientific reports · 2021Article
- Pandemics, epidemics, viruses, plagues, and disease: Comparative frequency analysis of a cultural pathology reflected in science fiction magazines from 1926 to 2015.Social sciences & humanities open · 2020Article
- Open Agile text mining for bioinformatics: the PubAnnotation ecosystem.Bioinformatics (Oxford, England) · 2019Article
- Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.Scientific reports · 2019Article
- The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations.Methods in molecular biology (Clifton, N.J.) · 2017Article
- The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions.Database : the journal of biological databases and curation · 2017Article
- Weakly supervised learning of biomedical information extraction from curated data.BMC bioinformatics · 2016Article
- Mining locus tags in PubMed Central to improve microbial gene annotation.BMC bioinformatics · 2014Article
- Assisting manual literature curation for protein-protein interactions using BioQRator.Database : the journal of biological databases and curation · 2014Article
- Review of biological network data and its applications.Genomics & informatics · 2013Review
- Towards precision medicine: advances in computational approaches for the analysis of human variants.Journal of molecular biology · 2013Review
- Chapter 15: disease gene prioritization.PLoS computational biology · 2013Article
- Text-mining solutions for biomedical research: enabling integrative biology.Nature reviews. Genetics · 2012Review
- Bioinformatics for personal genome interpretation.Briefings in bioinformatics · 2012Review
- My data are your data.Nature biotechnology · 2012Article
- Layout-aware text extraction from full-text PDF of scientific articles.Source code for biology and medicine · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 13 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article collects opinions from leading scientists about how text mining can provide better access to the biological literature, how the scientific community can help with this process, what the next steps are, and what role future BioCreative evaluations can play. The responses identify several broad themes, including the possibility of fusing literature and biological databases through text mining; the need for user interfaces tailored to different classes of users and supporting community-based annotation; the importance of scaling text mining technology and inserting it into larger workflows; and suggestions for additional challenge evaluations, new applications, and additional resources needed to make progress.
Indexed as
Identifiers
What 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.