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

An integrative multi-network and multi-classifier approach to predict genetic interactions.

PLoS computational biology - 9 Sept 2010

Pandey Gaurav, Zhang Bin, Chang Aaron N, Myers Chad L, Zhu Jun, Kumar Vipin, Schadt Eric E

Abstract excerpt

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of com...

Read the complete abstract on PubMed

Topics

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

Share to Area Under Curve
Share to Databases, Genetic
Share to Gene Regulatory Networks

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genes, Fungal

Share to Reproducibility of Results

Share to ROC Curve

Share to Saccharomyces cerevisiae Proteins

Share to Statistics, Nonparametric

Source

Open in PubMed Open full text

Identifiers

PMID: 20838583
DOI: 10.1371/journal.pcbi.1000928
PMCID: PMC2936518

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026. Prepared record publication-20838583-2026-09-02.2. Manifest publication-graph-cohort-2026-09-02.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

An integrative multi-network and multi-classifier approach to predict genetic interactions.PMID 20838583Read ↗

SSLPred: predicting synthetic sickness lethality.Semantic proximity Read ↗

Predictability of Genetic Interactions from Functional Gene ModulesSemantic proximity Read ↗

Multiple genetic interaction experiments provide complementary information useful for gene function prediction.Semantic proximity Read ↗

Combining biological networks to predict genetic interactions.Semantic proximity Read ↗

Human synthetic lethal inference as potential anti-cancer target gene detection.Semantic proximity Read ↗

Systematic analysis of complex genetic interactions.Semantic proximity Read ↗

Predicting genetic modifier loci using functional gene networks.Semantic proximity Read ↗

Global insights into protein complexes through integrated analysis of the reliable interactome and knockout lethality.Semantic proximity Read ↗

Differential analysis of high-throughput quantitative genetic interaction data.Semantic proximity Read ↗

SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis.Semantic proximity Read ↗

Beyond synthetic lethality: charting the landscape of clinically relevant genetic interactions in cancerSemantic proximity Read ↗

Genome-scale prediction of context-specific synthetic lethality beyond protein interaction networksSemantic proximity Read ↗

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