Evidence map›Paper›PMID 25617415›Full record

ArticleBioinformatics (Oxford, England)2015

ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways.

Ying Shen, Mumtahena Rahman, Stephen R Piccolo, Daniel Gusenleitner, Nader N El-Chaar, Luis Cheng, Stefano Monti, Andrea H Bild, W Evan Johnson

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Drug vector representation: a tool for drug similarity analysis.Molecular genetics and genomics : MGG · 2020
    Article
  11. Glucocorticoids rapidly activate cAMP production via GFASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2020
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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

9 authors.

Ying ShenDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
Mumtahena RahmanDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
Stephen R PiccoloDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
Daniel GusenleitnerDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
Nader N El-ChaarDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
Luis ChengDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
Stefano MontiDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
Andrea H BildDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.
W Evan JohnsonDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.

Funding

UNIVERSITY OF UTAH MEDICAL INFORMATICS TRAININGT15LM007124 · NLM · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Karen Louise Eilbeck · 1997 to 2026
$22.0M
Integrative signaling models to decipher complex cancer phenotypesU01CA164720 · NCI · UNIVERSITY OF UTAH · PI BILD, ANDREA HOPE, GRAY, JOE W. · 2012 to 2016
$2.9M
NCI NIH HHS U01 CA164720NCI NIH HHS U01CA164720NLM NIH HHS T15 LM007124NLM NIH HHS T15LM007124
6 · The paper itself

Abstract

motivationAlthough gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts-controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-profiling platforms often mask true biological variation. Addressing such challenges will enable us to better elucidate disease mechanisms and provide improved guidance for personalized therapeutics.

resultsHere, we present a pathway profiling toolkit, Adaptive Signature Selection and InteGratioN (ASSIGN), which enables robust and context-specific pathway analyses by efficiently capturing pathway activity in heterogeneous sets of samples and across profiling technologies. The ASSIGN framework is based on a flexible Bayesian factor analysis approach that allows for simultaneous profiling of multiple correlated pathways and for the adaptation of pathway signatures into specific disease. We demonstrate the robustness and versatility of ASSIGN in estimating pathway activity in simulated data, cell lines perturbed pathways and in primary tissues samples including The Cancer Genome Atlas breast carcinoma samples and liver samples exposed to genotoxic carcinogens. AVAILABILITY AND IMPLEMENTATION: Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.

Indexed as

SoftwareAnimalsBayes TheoremBreast NeoplasmsFemaleGene Expression ProfilingGenomicsHumansRatsSignal Transduction

Identifiers

PMID25617415
PMCPMC4443674

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.