ArticleBMC medical genomics2018
An integrated clinical and genomic information system for cancer precision medicine.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- How artificial intelligence is transforming nephrology.BMC nephrology · 2024Article
- Integrated multi-omics with machine learning to uncover the intricacies of kidney disease.Briefings in bioinformatics · 2024Review
- Knowledge bases and software support for variant interpretation in precision oncology.Briefings in bioinformatics · 2021Article
- A comprehensive database for integrated analysis of omics data in autoimmune diseases.BMC bioinformatics · 2021Article
- Big Data Approaches in Heart Failure Research.Current heart failure reports · 2020Review
- [Detection and interpretation of somatic variants in molecular pathology].Der Pathologe · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIncreasing affordability of next-generation sequencing (NGS) has created an opportunity for realizing genomically-informed personalized cancer therapy as a path to precision oncology. However, the complex nature of genomic information presents a huge challenge for clinicians in interpreting the patient's genomic alterations and selecting the optimum approved or investigational therapy. An elaborate and practical information system is urgently needed to support clinical decision as well as to test clinical hypotheses quickly.
resultsHere, we present an integrated clinical and genomic information system (CGIS) based on NGS data analyses. Major components include modules for handling clinical data, NGS data processing, variant annotation and prioritization, drug-target-pathway analysis, and population cohort explorer. We built a comprehensive knowledgebase of genes, variants, drugs by collecting annotated information from public and in-house resources. Structured reports for molecular pathology are generated using standardized terminology in order to help clinicians interpret genomic variants and utilize them for targeted cancer therapy. We also implemented many features useful for testing hypotheses to develop prognostic markers from mutation and gene expression data.
conclusionsOur CGIS software is an attempt to provide useful information for both clinicians and scientists who want to explore genomic information for precision oncology.
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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.