ReviewPerspectives in health information managemen2019
Genetic Variations and Precision Medicine.
Review in Perspectives in health information managemen, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Advances in fluorescence-based point-of-care diagnostics: probes, nanostructures and integrated systems.Journal of materials chemistry. C · 2026Review
- Efficient and easy gene expression and genetic variation data analysis and visualization using exvar.Scientific reports · 2025Article
- Genomic medicine and personalized treatment: a narrative review.Annals of medicine and surgery (2012) · 2025Review
- Creating largeFrontiers in plant science · 2025Article
- Article
- DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19.Computers in biology and medicine · 2023Article
- Advances in point-of-care genetic testing for personalized medicine applications.Biomicrofluidics · 2023Review
- Pan-cancer analysis of theOpen medicine (Warsaw, Poland) · 2023Article
- Central resources of variant discovery and annotation and its role in precision medicine.Asian biomedicine : research, reviews and news · 2022Review
- Genetic variants associated with the occurrence and progression of adolescent idiopathic scoliosis: a systematic review protocol.Systematic reviews · 2022Article
- From saliva to SNP: non-invasive, point-of-care genotyping for precision medicine applications using recombinase polymerase amplification and giant magnetoresistive nanosensors.Lab on a chip · 2022Article
- Article
- Implementation of a Renal Precision Medicine Program: Clinician Attitudes and Acceptance.Life (Basel, Switzerland) · 2020Article
- Public Attitudes Toward Precision Medicine: A Nationwide Survey on Developing a National Cohort Program for Citizen Participation in the Republic of Korea.Frontiers in genetics · 2020Article
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
The time and costs associated with the sequencing of a human genome have decreased significantly in recent years. Many people have chosen to have their genomes sequenced to receive genomics-based personalized healthcare services. To reach the goal of genomics-based precision medicine, health information management (HIM) professionals need to manage and analyze patients' genomic data. Two important pieces of information from the genome sequence are the risk of genetic diseases and the specific medication or pharmacogenomic results for the individual patient, both of which are linked to a patient's genetic variations. In this review article, we introduce genetic variations, including their data types, relevant databases, and some currently available analysis methods and systems. HIM professionals can choose to use these databases, methods, and systems in the management and analysis of patients' genomic data.
Indexed as
Identifiers
31019429PMC6462879What 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.