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

Learning a prior on regulatory potential from eQTL data.

PLoS genetics - 1 Jan 2009

Lee Su-In, Dudley Aimée M, Drubin David, Silver Pamela A, Krogan Nevan J, Pe'er Dana, Koller Daphne

Abstract excerpt

Genome-wide RNA expression data provide a detailed view of an organism's biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. However, with data from a relatively small number of individuals, it is difficult to distinguish true causal polymorphisms from the large number of...

Read the complete abstract on PubMed

Topics

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

Share to Yeasts
Share to Software
Share to Algorithms

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Computational Biology

Share to Databases, Nucleic Acid

Share to Gene Regulatory Networks

Share to Oligonucleotide Array Sequence Analysis

Share to Quantitative Trait Loci

Source

Open in PubMed Open full text

Identifiers

PMID: 19180192
DOI: 10.1371/journal.pgen.1000358
PMCID: PMC2627940

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Learning a prior on regulatory potential from eQTL data.PMID 19180192Read ↗

Efficient and accurate causal inference with hidden con-founders from genome-transcriptome variation dataSemantic proximity Read ↗

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data.Semantic proximity Read ↗

Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human GeneticsSemantic proximity Read ↗

Predicting target genes of non-coding regulatory variants with IRT.Semantic proximity Read ↗

reg-eQTL: Integrating transcription factor effects to unveil regulatory variants.Semantic proximity Read ↗

Dissecting the regulatory architecture of gene expression QTLs.Semantic proximity Read ↗

Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.Semantic proximity Read ↗

Leveraging supervised learning for functionally-informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLsSemantic proximity Read ↗

Using stochastic causal trees to augment Bayesian networks for modeling eQTL datasets.Semantic proximity Read ↗

Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs.Semantic proximity Read ↗

Semi-supervised learning improves regulatory sequence prediction with unlabeled sequencesSemantic proximity Read ↗

CERENKOV3: Clustering and molecular network-derived features improve computational prediction of functional noncoding SNPs.Semantic proximity Read ↗

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