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iPOP specifications

Information


Unique identifier OMICS_25554
Name iPOP
Alternative name integrated Personal Omics Profiling
Restrictions to use None
Community driven No
Data access File download
User data submission Not allowed
Maintained Yes

Maintainer


  • person_outline Michael Snyder

Additional information


http://snyderlab.stanford.edu/iPOP.html

Publication for integrated Personal Omics Profiling

iPOP citations

 (6)
library_books

A Path to Implement Precision Child Health Cardiovascular Medicine

2017
PMCID: 5451507
PMID: 28620608
DOI: 10.3389/fcvm.2017.00036

[…] ism spectrum disorders () and cancer biology () for integrating multilayered clinical data and genomics networks. Other intriguing systems based methods include the Integrated Personal Omics Profile (IPOP) for personal omics for dynamic data integration along the clinical course (), allowing acquiring a comprehensive picture of detailed molecular differences between different physiological states […]

library_books

Proteomics and integrative omic approaches for understanding host–pathogen interactions and infectious diseases

2017
Mol Syst Biol
PMCID: 5371729
PMID: 28348067
DOI: 10.15252/msb.20167062

[…] holipids and lipid‐regulating enzymes that act as hubs and are linked to HCV pathogenesis. The value of integrating multi‐omics datasets is also highlighted by the integrative personal omics profile (iPOP) analysis, which collected multi‐omic data of an individual throughout a 14‐month period that included two events of viral infection (Chen et al, ). Other studies have integrated phenotypic data […]

library_books

Other side of the coin for personalised medicine and healthcare: content analysis of ‘personalised’ practices in the literature

2016
BMJ Open
PMCID: 4947721
PMID: 27412099
DOI: 10.1136/bmjopen-2015-010243

[…] ome will evolve in different ways that have not been identified in this study. Targeted research using different data sources can explore how the practices evolve and transform over time.For example, iPOP (P-50) and an example of ‘quantified-self’ movement by Smarr (P-59) are categorised as emerging practices (category-3) in axis-1. They are single cases that use longitudinal health data, in line […]

library_books

Circulating microbial RNA and health

2015
Sci Rep
PMCID: 4649493
PMID: 26576508
DOI: 10.1038/srep16814

[…] Although within-subject similarity was obvious in the individual recruited in the iPOP study, within-subject similarity was violated for the patients in the LVAD study. The bacterial gene expression profiles among individuals were synchronized at 180 days post implantation (). The […]

library_books

A review on computational systems biology of pathogen–host interactions

2015
Front Microbiol
PMCID: 4391036
PMID: 25914674
DOI: 10.3389/fmicb.2015.00235

[…] eutics, and vaccines. Thus, systems biology of infection allows to yield novel therapeutic targets () and to establish individualized or personalized medicine. The integrative personal omics profile (iPOP) combines genomics, transcriptomics, proteomics, metabolomics, and autoantibody profiles from a single individual over a 14-month period (; ).There are various platforms for handling of measured […]

library_books

Jumping on the Train of Personalized Medicine: A Primer for Non Geneticist Clinicians: Part 3. Clinical Applications in the Personalized Medicine Area

2014
PMCID: 4287884
PMID: 25598768
DOI: 10.2174/1573400510666140630170549

[…] ch measures other “omics” profiling with different high-throughput platforms will theoretically improve personalized medicine. Recently, Chen et al. first used “integrative personal omics profiling” (iPOP), which included genomics, transcriptomic, proteomic, metabolomics and autoantibody profiles, to evaluate healthy and diseased status []. They collected blood samples from a 54-year-old male volu […]

Citations

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iPOP institution(s)
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Stanford University, Stanford, CA, USA; Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Telomeres and Telomerase Group, Molecular Oncology Program, Spanish National Cancer Centre (CNIO), Madrid, Spain; Life Length, Madrid, Spain; Division of Hematology, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Computer Science, Yale University, New Haven, CT, USA
iPOP funding source(s)
Supported by grants from Stanford University and the NIH; by the NIH/NLM training grant T15- LM007033; by NIH/NIGMS R24-GM61374; by the Spanish Ministry of Science and Innovation Projects SAF2008-05384 and CSD2007-00017, European Union FP7 Projects 2007-A-201630 (GENICA) and 2007-A-200950 (TELOMARKER), European Research Council Advanced Grant GA#232854, the Körber Foundation, the Fundación Marcelino Botín and Fundación Lilly (España); by NIH/NHLBI training grant T32 HL094274; by NIH/ NHLBI KO8 HL083914, NIH New Investigator DP2 Award OD004613, and a grant from the Breetwor Family Foundation.

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