ArticlePLoS computational biology2009
Biomedical discovery acceleration, with applications to craniofacial development.
Article in PLoS computational biology, 2009. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 60 citations in OpenAlex.
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- The gastrin and cholecystokinin receptors mediated signaling network: a scaffold for data analysis and new hypotheses on regulatory mechanisms.BMC systems biology · 2015Article
- Visual analysis of biological data-knowledge networks.BMC bioinformatics · 2015Article
- Biomedical question answering using semantic relations.BMC bioinformatics · 2015Article
- Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks.Journal of biomedical semantics · 2014Article
- Proteomics and metabolomics in renal transplantation-quo vadis?Transplant international : official journal of the European Society for Organ Transplantation · 2013Review
- Text-mining solutions for biomedical research: enabling integrative biology.Nature reviews. Genetics · 2012Review
- Synergizing genomic analysis with biological knowledge to identify and validate novel genes in pancreatic development.Pancreas · 2012Article
- Concept annotation in the CRAFT corpus.BMC bioinformatics · 2012Article
- Mining the pharmacogenomics literature--a survey of the state of the art.Briefings in bioinformatics · 2012Article
- Protein carbonylation in a murine model for early alcoholic liver disease.Chemical research in toxicology · 2012Article
- Metab2MeSH: annotating compounds with medical subject headings.Bioinformatics (Oxford, England) · 2012Article
- HIGH-PRECISION BIOLOGICAL EVENT EXTRACTION: EFFECTS OF SYSTEM AND OF DATA.Computational intelligence · 2011Article
- Translational web robots for pathogen genome analysis.Microbial informatics and experimentation · 2011Article
- Advancing science through mining libraries, ontologies, and communities.The Journal of biological chemistry · 2011Review
- Discovering and visualizing indirect associations between biomedical concepts.Bioinformatics (Oxford, England) · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.
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.