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Article

Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data.

Scientific reports - 23 Nov 2017

Lu Xinguo, Lu Jibo, Liao Bo, Li Xing, Qian Xin, Li Keqin

Abstract excerpt

Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Source

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Identifiers

PMID: 29170526
DOI: 10.1038/s41598-017-16286-5
PMCID: PMC5700962

Genes

APOB
BRCA1
CCL11
CCL16
CCL18
CCL23
CCL5
CCL8
FGF22
GNA11
GNA15

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.

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