Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment

PLoS Computational Biology

Volumn 9, Issue 3, 2013, Pages

  Zhang, Wei ^a   Ota, Takayo ^b   Shridhar, Viji ^b   Chien, Jeremy ^b   Wu, Baolin ^c   Kuang, Rui ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

^b Mayo Clinic College of Medicine   (United States)

^c UNIVERSITY OF MINNESOTA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; DISEASES; FORECASTING; GENE EXPRESSION; REGRESSION ANALYSIS;

CO-EXPRESSION; COX MODEL; FUNCTIONAL RELATION; GENE NETWORKS; NETWORK INFORMATION; NETWORK-BASED; OVARIAN CANCERS; SIGNATURE GENES; SUBNETWORKS; SURVIVAL ANALYSIS;

CHEMOTHERAPY;

FIBRILLIN 1; MITOGEN ACTIVATED PROTEIN KINASE; PLATINUM COMPLEX; SCLEROPROTEIN;

ARTICLE; CANCER PROGNOSIS; CANCER RECURRENCE; CANCER RESISTANCE; CANCER RISK; CANCER SURVIVAL; COHORT ANALYSIS; FBN1 GENE; FEMALE; GENE; GENE EXPRESSION; GENE IDENTIFICATION; GENE REGULATORY NETWORK; GENETIC DATABASE; GENETIC RISK; GENETIC SELECTION; HUMAN; HUMAN TISSUE; INTRACELLULAR SIGNALING; MICROARRAY ANALYSIS; NUCLEOTIDE SEQUENCE; OVARY CANCER; PROPORTIONAL HAZARDS MODEL; PROTEIN EXPRESSION; PROTEIN PROTEIN INTERACTION; RISK ASSESSMENT; SURVIVAL PREDICTION; TREATMENT OUTCOME; VALIDATION STUDY;

EID: 84875981977     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002975     Document Type: Article

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