Evidence map›Paper›PMID 25798291›Full record

ArticleQuantitative biology (Beijing, China)2013

Personal genomes, quantitative dynamic omics and personalized medicine.

George I Mias, Michael Snyder

Registry-linked trialOpen access · diamondAbstract read
In one paragraph

Article in Quantitative biology (Beijing, China), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02654704 (Vaccination Responses in Young and Older Adults), which is not on this map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
1 · What the graph read from 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.

2 · The registry

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.

NCT02654704 terminatednot on this mapstarted 2015, after this paper: background citation

Vaccination Responses in Young and Older Adults

TypeobservationalSponsorMichigan State UniversityRan2015 to 2020Enrolled61ConditionsIndividuality, Healthy, Asthma, Immune System and Related DisordersArmsPneumococcal Vaccination
3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Precision medicine in diabetes: an opportunity for clinical translation.Annals of the New York Academy of Sciences · 2018
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Cancer genomics & proteomics
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

George I MiasDepartment of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA.
Michael SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA.
Stanford University · US

Funding

INSTITUTIONAL TRAINING GRANT IN GENOME SCIENCET32HG000044 · NHGRI · STANFORD UNIVERSITY · PI MICHAEL P. SNYDER · 1995 to 2026
$32.2M
Production Center for Mapping Regulatory Regions of the Human GenomeU54HG006996 · NHGRI · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P. · 2012 to 2016
$14.6M
Longitudinal Multiomics Microbial Profiling in Healthy and Disease IndividualsU54DK102556 · NIDDK · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P., WEINSTOCK, GEORGE M · 2014 to 2015
$7.7M
NHGRI NIH HHS T32 HG000044NHGRI NIH HHS U54 HG006996NIDDK NIH HHS U54 DK102556
6 · The paper itself

Abstract

The rapid technological developments following the Human Genome Project have made possible the availability of personalized genomes. As the focus now shifts from characterizing genomes to making personalized disease associations, in combination with the availability of other omics technologies, the next big push will be not only to obtain a personalized genome, but to quantitatively follow other omics. This will include transcriptomes, proteomes, metabolomes, antibodyomes, and new emerging technologies, enabling the profiling of thousands of molecular components in individuals. Furthermore, omics profiling performed longitudinally can probe the temporal patterns associated with both molecular changes and associated physiological health and disease states. Such data necessitates the development of computational methodology to not only handle and descriptively assess such data, but also construct quantitative biological models. Here we describe the availability of personal genomes and developing omics technologies that can be brought together for personalized implementations and how these novel integrated approaches may effectively provide a precise personalized medicine that focuses on not only characterization and treatment but ultimately the prevention of disease.

Identifiers

PMID25798291
PMCPMC4366006
OpenAlexW2033641561

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.