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

Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

1 Oct 2010

Abstract excerpt

BACKGROUND: Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL...

Read the complete abstract on PubMed

Topics

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

Share to Bioinformatics and Genomic Networks
Share to Chronic Lymphocytic Leukemia Research
Share to Ferroptosis and cancer prognosis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 21044363
DOI: 10.1186/1471-2105-11-s9-s5

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemiaPMID 21044363Read ↗

The network structure of hematopoietic cancersSemantic proximity Read ↗

Novel Genomic Biomarker Candidates for Cervical Cancer As Identified by Differential Co-Expression Network AnalysisSemantic proximity Read ↗

COBLL1, LPL and ZAP70 expression defines prognostic subgroups of chronic lymphocytic leukemia patients with high accuracy and correlates with IGHV mutational status.Semantic proximity Read ↗

Prognostic genes of breast cancer revealed by gene co-expression network analysisSemantic proximity Read ↗

Genomic data integration in chronic lymphocytic leukemia.Semantic proximity Read ↗

Incorporating gene co-expression network in identification of cancer prognosis markersSemantic proximity Read ↗

A data-driven approach to build a predictive model of cancer patients' disease outcome by utilizing co-expression networksSemantic proximity Read ↗

Analysis of chronic lymphotic leukemia transcriptomic profile: differences between molecular subgroups.Semantic proximity Read ↗

In-silico identification of genetic variants associated with chronic lymphocytic leukemia for diagnostic and therapeutic applications.Semantic proximity Read ↗

The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AMLSemantic proximity Read ↗

The Landscape of Gene Co-Expression Modules Correlating with Prognostic Genetic Abnormalities in AMLSemantic proximity Read ↗

Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA dataSemantic proximity Read ↗

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