Protein subnetwork markers improve prediction of cancer outcome

Molecular Systems Biology

Volumn 3, Issue , 2007, Pages

  Auffray, Charles ^a  

^a SORBONNE UNIVERSITÉ   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; MESSENGER RNA; PROTEIN SUBNETWORK MARKER; UNCLASSIFIED DRUG; TUMOR MARKER; TUMOR PROTEIN;

CANCER; CANCER SUSCEPTIBILITY; DATA ANALYSIS; GENE EXPRESSION; GENE MUTATION; METASTASIS; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; PROTEIN INTERACTION; PROTEIN PROTEIN INTERACTION; SHORT SURVEY; STATISTICAL ANALYSIS; TREATMENT OUTCOME; GENE EXPRESSION PROFILING; GENETIC MARKER; GENETICS; HUMAN; NEOPLASM; NOTE; PREDICTION AND FORECASTING;

GENE EXPRESSION PROFILING; GENETIC MARKERS; HUMANS; NEOPLASM PROTEINS; NEOPLASMS; PREDICTIVE VALUE OF TESTS; TREATMENT OUTCOME; TUMOR MARKERS, BIOLOGICAL;

EID: 35348819954     PISSN: 17444292     EISSN: 17444292     Source Type: Journal    
DOI: 10.1038/msb4100183     Document Type: Short Survey

References (11)

1. Chuang H-Y, Lee E, Liu Y-T, Lee D, Ideker T (2007) Network-based classification of breast cancer metastasis. Mol Syst Biol 3: 140
2. Dupuy A, Simon R (2007) Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Nat Cancer Inst 99: 147-157
3. Ein-Dor L, Zuk O, Domany E (2006) Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci USA 103: 5923-5928
4. Hwang D, Smith JJ, Leslie DM, Weston AD, Rust AG, Ramsey S, de Atauri P, Siegel AF, Bolouri H, AItchison JD, Hood L (2005) A data integration methodology for systems biology. Proc Natl Acad Sci USA 102: 17302-17307
5. Michiels S, Koscielny S, Hill C (2005) Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365: 488-492
6. Ntzani E, Ioannidis JP (2003) Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment. Lancet 362: 1439-1444
7. Poyatos JF, Hurst LD (2004) How biologically relevant are interaction-based modules in protein networks? Genome Biol 5: R93
8. Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S et al (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature 437: 1173-1178
9. Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, Friedman N (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34: 166-176
10. van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530-536
11. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, Foekens JA (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365: 671-679