ArticleJournal of the American Medical Informatics Association : JAMIA2016
Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing.JCO clinical cancer informatics · 2019Pooled it
- Histopathological Subtypes of Cutaneous Melanoma: Prognostic and Molecular Implications.Cureus · 2025Review
- Searching of Clinical Trials Made Easier in cBioPortal Using Patients' Genetic and Clinical Profiles.Applied clinical informatics · 2022Article
- Knowledge bases and software support for variant interpretation in precision oncology.Briefings in bioinformatics · 2021Article
- Practical Aspects of Implementing and Applying Health Care Cloud Computing Services and Informatics to Cancer Clinical Trial Data.JCO clinical cancer informatics · 2021Article
- The My Cancer Genome clinical trial data model and trial curation workflow.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Unique insights from ClinicalTrials.gov by mining protein mutations and RSids in addition to applying the Human Phenotype Ontology.PloS one · 2020Article
- OCTANE: Oncology Clinical Trial Annotation Engine.JCO clinical cancer informatics · 2019Article
- A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.Genomics & informatics · 2019Article
- Next-Generation Sequencing and the Clinical Oncology Workflow: Data Challenges, Proposed Solutions, and a Call to Action.JCO precision oncology · 2019Review
- Personalized cancer therapy-leveraging a knowledge base for clinical decision-making.Cold Spring Harbor molecular case studies · 2018Review
- Improving precision in concept normalization.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing.Yearbook of medical informatics · 2017Review
- CATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice.CPT: pharmacometrics & systems pharmacology · 2017Article
- Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials.Assay and drug development technologies · 2016Article
- Precision medicine informatics.Journal of the American Medical Informatics Association : JAMIA · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
objectiveClinical trials investigating drugs that target specific genetic alterations in tumors are important for promoting personalized cancer therapy. The goal of this project is to create a knowledge base of cancer treatment trials with annotations about genetic alterations from ClinicalTrials.gov.
methodsWe developed a semi-automatic framework that combines advanced text-processing techniques with manual review to curate genetic alteration information in cancer trials. The framework consists of a document classification system to identify cancer treatment trials from ClinicalTrials.gov and an information extraction system to extract gene and alteration pairs from the Title and Eligibility Criteria sections of clinical trials. By applying the framework to trials at ClinicalTrials.gov, we created a knowledge base of cancer treatment trials with genetic alteration annotations. We then evaluated each component of the framework against manually reviewed sets of clinical trials and generated descriptive statistics of the knowledge base. RESULTS AND DISCUSSION: The automated cancer treatment trial identification system achieved a high precision of 0.9944. Together with the manual review process, it identified 20 193 cancer treatment trials from ClinicalTrials.gov. The automated gene-alteration extraction system achieved a precision of 0.8300 and a recall of 0.6803. After validation by manual review, we generated a knowledge base of 2024 cancer trials that are labeled with specific genetic alteration information. Analysis of the knowledge base revealed the trend of increased use of targeted therapy for cancer, as well as top frequent gene-alteration pairs of interest. We expect this knowledge base to be a valuable resource for physicians and patients who are seeking information about personalized cancer therapy.
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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.