Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

A bipartite network approach to inferring interactions between environmental exposures and human diseases.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing - 1 Jan 2015

Darabos Christian, Grussing Emily D, Cricco Maria E, Clark Kenzie A, Moore Jason H

Abstract excerpt

Environmental exposure is a key factor of understanding health and diseases. Beyond genetic propensities, many disorders are, in part, caused by human interaction with harmful substances in the water, the soil, or the air. Limited data is available on a disease or substance basis. However, we compile a global repository from literature surveys matching environmental chemical substances exposure with human...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Computational Biology
Share to Disease

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Environmental Exposure

Share to Environmental Pollutants

Share to Genome-Wide Association Study

Share to Phenotype

Source

Identifiers

PMID: 25592579

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A bipartite network approach to inferring interactions between environmental exposures and human diseases.PMID 25592579Read ↗

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

Disentangling genetic and environmental risk factors for individual diseases from multiplex comorbidity networks.Semantic proximity Read ↗

Using Environmental Mixture Exposure-Triggered Biological Knowledge-Driven Machine Learning to Predict Early Pregnancy LossSemantic proximity Read ↗

Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical ExposuresSemantic proximity Read ↗

A computational approach to chemical etiologies of diabetes.Semantic proximity Read ↗

The ENDOMIX project: an interdisciplinary approach to understanding how real-life chemical mixtures target the immune system to trigger diseaseSemantic proximity Read ↗

The ENDOMIX project: an interdisciplinary approach to understanding how real-life chemical mixtures target the immune system to trigger diseaseSemantic proximity Read ↗

A chemo-centric view of human health and disease.Semantic proximity Read ↗

CTD tetramers: a new online tool that computationally links curated chemicals, genes, phenotypes, and diseases to inform molecular mechanisms for environmental health.Semantic proximity Read ↗

Endocrine disrupting chemicals and COVID-19 relationships: a computational systems biology approachSemantic proximity Read ↗

Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental healthSemantic proximity Read ↗

Linking chemical data from the Comparative Toxicogenomics Database with adverse outcome pathways from the AOP-Wiki: a mechanistic data-oriented approach to help inform environmental healthSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.