ReviewPharmacogenomics2010
Recent progress in automatically extracting information from the pharmacogenomic literature.
Review in Pharmacogenomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Decade of Pharmacogenetic Studies in Jordan: A Systemic Review.The pharmacogenomics journal · 2021Pooled it
- Pooled it
- The E. coli Whole-Cell Modeling Project.EcoSal Plus · 2021Article
- PharmGKB, an Integrated Resource of Pharmacogenomic Knowledge.Current protocols · 2021Article
- PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison.BMC bioinformatics · 2019Article
- Global Text Mining and Development of Pharmacogenomic Knowledge Resource for Precision Medicine.Frontiers in pharmacology · 2019Article
- PubCaseFinder: A Case-Report-Based, Phenotype-Driven Differential-Diagnosis System for Rare Diseases.American journal of human genetics · 2018Article
- Classification and analysis of a large collection of in vivo bioassay descriptions.PLoS computational biology · 2017Article
- Learning from biomedical linked data to suggest valid pharmacogenes.Journal of biomedical semantics · 2017Article
- Harnessing Biomedical Natural Language Processing Tools to Identify Medicinal Plant Knowledge from Historical Texts.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- eGARD: Extracting associations between genomic anomalies and drug responses from text.PloS one · 2017Article
- ARN: analysis and prediction by adipogenic professional database.BMC systems biology · 2016Article
- Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery.Briefings in bioinformatics · 2016Review
- Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.BMC bioinformatics · 2015Article
- A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set.Computational and mathematical methods in medicine · 2015Article
- A semi-supervised approach to extract pharmacogenomics-specific drug-gene pairs from biomedical literature for personalized medicine.Journal of biomedical informatics · 2013Article
- Open issues in intelligent personal health record--an updated status report for 2012.Journal of medical systems · 2013Article
- Discovery of novel biomarkers and phenotypes by semantic technologies.BMC bioinformatics · 2013Article
- Negated bio-events: analysis and identification.BMC bioinformatics · 2013Article
- An iterative searching and ranking algorithm for prioritising pharmacogenomics genes.International journal of computational biology and drug design · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The biomedical literature holds our understanding of pharmacogenomics, but it is dispersed across many journals. In order to integrate our knowledge, connect important facts across publications and generate new hypotheses we must organize and encode the contents of the literature. By creating databases of structured pharmocogenomic knowledge, we can make the value of the literature much greater than the sum of the individual reports. We can, for example, generate candidate gene lists or interpret surprising hits in genome-wide association studies. Text mining automatically adds structure to the unstructured knowledge embedded in millions of publications, and recent years have seen a surge in work on biomedical text mining, some specific to pharmacogenomics literature. These methods enable extraction of specific types of information and can also provide answers to general, systemic queries. In this article, we describe the main tasks of text mining in the context of pharmacogenomics, summarize recent applications and anticipate the next phase of text mining applications.
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.