ArticleBioinformatics (Oxford, England)2012
BioContext: an integrated text mining system for large-scale extraction and contextualization of biomolecular events.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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.
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Who cites it
29 citing papers in PubMed.
- Cancer-Alterome: a literature-mined resource for regulatory events caused by genetic alterations in cancer.Scientific data · 2024Article
- GePI: large-scale text mining, customized retrieval and flexible filtering of gene/protein interactions.Nucleic acids research · 2023Article
- Text mining for modeling of protein complexes enhanced by machine learning.Bioinformatics (Oxford, England) · 2021Article
- Causal Inference Engine: a platform for directional gene set enrichment analysis and inference of active transcriptional regulators.Nucleic acids research · 2019Article
- Constructing a database for the relations between CNV and human genetic diseases via systematic text mining.BMC bioinformatics · 2018Article
- ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.Molecules (Basel, Switzerland) · 2018Article
- Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies.Computers in biology and medicine · 2018Article
- iTextMine: integrated text-mining system for large-scale knowledge extraction from the literature.Database : the journal of biological databases and curation · 2018Article
- Article
- Gold-standard ontology-based anatomical annotation in the CRAFT Corpus.Database : the journal of biological databases and curation · 2017Article
- Toward patient-tailored summarization of lung cancer literature.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2016Article
- Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.Database : the journal of biological databases and curation · 2016Article
- Filtering large-scale event collections using a combination of supervised and unsupervised learning for event trigger classification.Journal of biomedical semantics · 2016Article
- Article
- Constructing a molecular interaction network for thyroid cancer via large-scale text mining of gene and pathway events.BMC systems biology · 2015Article
- Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations.Journal of cheminformatics · 2015Article
- Self-training in significance space of support vectors for imbalanced biomedical event data.BMC bioinformatics · 2015Article
- Sequential pattern mining for discovering gene interactions and their contextual information from biomedical texts.Journal of biomedical semantics · 2015Article
- Sperm Proteome Maturation in the Mouse Epididymis.PloS one · 2015Article
- Large-scale modeling of condition-specific gene regulatory networks by information integration and inference.Nucleic acids research · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
motivationAlthough the amount of data in biology is rapidly increasing, critical information for understanding biological events like phosphorylation or gene expression remains locked in the biomedical literature. Most current text mining (TM) approaches to extract information about biological events are focused on either limited-scale studies and/or abstracts, with data extracted lacking context and rarely available to support further research.
resultsHere we present BioContext, an integrated TM system which extracts, extends and integrates results from a number of tools performing entity recognition, biomolecular event extraction and contextualization. Application of our system to 10.9 million MEDLINE abstracts and 234 000 open-access full-text articles from PubMed Central yielded over 36 million mentions representing 11.4 million distinct events. Event participants included over 290 000 distinct genes/proteins that are mentioned more than 80 million times and linked where possible to Entrez Gene identifiers. Over a third of events contain contextual information such as the anatomical location of the event occurrence or whether the event is reported as negated or speculative. AVAILABILITY: The BioContext pipeline is available for download (under the BSD license) at http://www.biocontext.org, along with the extracted data which is also available for online browsing.
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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.