ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium
Automated Metabolic Phenotyping of Cytochrome Polymorphisms Using PubMed Abstract Mining.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- PGxCorpus, a manually annotated corpus for pharmacogenomics.Scientific data · 2020Article
- Pharmacogenomic Approaches for Automated Medication Risk Assessment in People with Polypharmacy.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
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
No grant is acknowledged in the PubMed record.
Abstract
Pharmacogenetics-related publications, which are increasing rapidly, provide important new pharmacogenetics knowledge. Automated approaches to extract information of new alleles and to identify their impact on metabolic phenotypes from publications are urgently needed to facilitate personalized medicine and improve clinical outcomes. Cytochrome polymorphisms, responsible for a wide variation of drug pharmacodynamics, individual efficacy and adverse effects, have significant potential for optimizing drug therapy. A few studies have addressed specialized efforts to automatically extract cytochrome polymorphisms and their characterizations regarding metabolic phenotypes from the literature. In this paper, we present a novel rule-based text-mining system to extract metabolic phenotypes of polymorphisms from PubMed abstracts with a focus on cytochrome P450. This system is promising as it achieved a precision of 85.71% in a preliminary proof-of-concept evaluation and is expected to automatically provide up-to-date metabolic information for cytochrome polymorphisms, which is critical to advance personalized medicine and improve clinical care.
Indexed as
Identifiers
29854118PMC5977704What 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.