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

Nested effects models for high-dimensional phenotyping screens.

Bioinformatics (Oxford, England) - 1 Jul 2007

Markowetz Florian, Kostka Dennis, Troyanskaya Olga G, Spang Rainer

Abstract excerpt

MOTIVATION: In high-dimensional phenotyping screens, a large number of cellular features is observed after perturbing genes by knockouts or RNA interference. Comprehensive analysis of perturbation effects is one of the most powerful techniques for attributing functions to genes, but not much work has been done so far to adapt statistical and computational methodology to the specific needs of large-scale and...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Gene Expression Profiling
Share to Gene Expression Regulation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Biological

Share to Protein Interaction Mapping

Share to Proteome

Share to Signal Transduction

Share to Software

Source

Identifiers

PMID: 17646311
DOI: 10.1093/bioinformatics/btm178

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

Nested effects models for high-dimensional phenotyping screens.PMID 17646311Read ↗

Inferring subnetworks from perturbed expression profiles.Semantic proximity Read ↗

Structure learning in Nested Effects Models.Semantic proximity Read ↗

Learning epistatic gene interactions from perturbation screensSemantic proximity Read ↗

Noise reduction in genome-wide perturbation screens using linear mixed-effect models.Semantic proximity Read ↗

KDML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screensSemantic proximity Read ↗

Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.Semantic proximity Read ↗

Efficient strategies for screening large-scale genetic interaction networksSemantic proximity Read ↗

Predictability of Genetic Interactions from Functional Gene ModulesSemantic proximity Read ↗

Quantitative epistasis analysis and pathway inference from genetic interaction data.Semantic proximity Read ↗

A novel computational approach for predicting complex phenotypes in Drosophila (starvation-sensitive and sterile) by deriving their gene expression signatures from public data.Semantic proximity Read ↗

Single cell network analysis with a mixture of Nested Effects ModelsSemantic proximity Read ↗

modPhEA: model organism Phenotype Enrichment Analysis of eukaryotic gene sets.Semantic proximity Read ↗

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