Evidence map›Paper›PMID 26776169›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2016

AN INTEGRATED NETWORK APPROACH TO IDENTIFYING BIOLOGICAL PATHWAYS AND ENVIRONMENTAL EXPOSURE INTERACTIONS IN COMPLEX DISEASES.

Christian Darabos, Jingya Qiu, Jason H Moore

Open access · goldAbstract read
In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 33% 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.

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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. 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

3 authors at 2 institutions in 1 country.

Christian DarabosThe Geisel School of Medicine, Dartmouth College, Lebanon, NH 03756, U.S.A2The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
Jingya Qiu
Jason H Moore
Dartmouth College · USUniversity of Pennsylvania · US

Funding

Translational Research Support CoreP30ES013508 · NIEHS · UNIVERSITY OF PENNSYLVANIA · PI Rebecca A Simmons · 2006 to 2026
$35.3M
Bioinformatics Strategies for Genome-Wide Association StudiesR01LM010098 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., WILLIAMS, SCOTT MATTHEW · 2009 to 2023
$5.1M
Bioinformatics Strategies: Biodefense Vaccine ResearchR01AI059694 · NIAID · DARTMOUTH COLLEGE · PI MOORE, JASON H. · 2004 to 2012
$4.5M
Machine Learning Prediction of Cancer SusceptibilityR01LM009012 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2006 to 2014
$3.3M
Bioinformatics Strategies for Multidimensional Brain Imaging GeneticsR01LM011360 · NLM · INDIANA UNIVERSITY INDIANAPOLIS · PI MOORE, JASON H., SAYKIN, ANDREW J · 2012 to 2015
$1.4M
Bioinformatics Approaches to Visual Disease GeneticsR01EY022300 · NEI · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2012 to 2014
$947k
NEI NIH HHS R01 EY022300NIAID NIH HHS AI59694NIAID NIH HHS R01 AI059694NIEHS NIH HHS P30 ES013508NLM NIH HHS LM009012NLM NIH HHS LM010098NLM NIH HHS R01 LM009012NLM NIH HHS R01 LM010098NLM NIH HHS R01 LM011360
6 · The paper itself

Abstract

Complex diseases are the result of intricate interactions between genetic, epigenetic and environmental factors. In previous studies, we used epidemiological and genetic data linking environmental exposure or genetic variants to phenotypic disease to construct Human Phenotype Networks and separately analyze the effects of both environment and genetic factors on disease interactions. To better capture the intricacies of the interactions between environmental exposure and the biological pathways in complex disorders, we integrate both aspects into a single "tripartite" network. Despite extensive research, the mechanisms by which chemical agents disrupt biological pathways are still poorly understood. In this study, we use our integrated network model to identify specific biological pathway candidates possibly disrupted by environmental agents. We conjecture that a higher number of co-occurrences between an environmental substance and biological pathway pair can be associated with a higher likelihood that the substance is involved in disrupting that pathway. We validate our model by demonstrating its ability to detect known arsenic and signal transduction pathway interactions and speculate on candidate cell-cell junction organization pathways disrupted by cadmium. The validation was supported by distinct publications of cell biology and genetic studies that associated environmental exposure to pathway disruption. The integrated network approach is a novel method for detecting the biological effects of environmental exposures. A better understanding of the molecular processes associated with specific environmental exposures will help in developing targeted molecular therapies for patients who have been exposed to the toxicity of environmental chemicals.

Indexed as

Environmental ExposureComputational BiologyDiseaseEpigenesis, GeneticGene-Environment InteractionGenome-Wide Association StudyHumansModels, BiologicalNonlinear DynamicsPhenotypePolymorphism, Single NucleotideSignal TransductionSystems Biology

Identifiers

PMID26776169
PMCPMC4721271
OpenAlexW2319040910

What OpenQuestion holds

Textmetadata
LicenceTDM
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.