Improving the efficiency of biomarker identification using biological knowledge

Pacific Symposium on Biocomputing 2009, PSB 2009

Volumn , Issue , 2009, Pages 427-438

  Phan, John H ^a   Yin Goen, Qiqin ^b   Young, Andrew N ^b   Wang, May D ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b EMORY UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMARKER IDENTIFICATION; DATA SETS; DIFFERENTIAL EXPRESSIONS; FEATURE RANKING; GENE EXPRESSION DATA; HIGH-THROUGHPUT; MICROARRAY DATA; PUBLIC DATABASE;

DISEASES; GENE EXPRESSION; OPTIMIZATION;

BIOMARKERS;

BIOLOGICAL MARKER; TUMOR MARKER;

ARTICLE; BIOMETRY; CLASSIFICATION; DNA MICROARRAY; GENETIC DATABASE; GENETIC MARKER; GENETICS; HUMAN; KIDNEY TUMOR; KNOWLEDGE BASE; NEOPLASM; REVERSE TRANSCRIPTION POLYMERASE CHAIN REACTION; STATISTICS; VALIDATION STUDY;

BIOLOGICAL MARKERS; BIOMETRY; DATABASES, GENETIC; GENETIC MARKERS; HUMANS; KIDNEY NEOPLASMS; KNOWLEDGE BASES; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REVERSE TRANSCRIPTASE POLYMERASE CHAIN REACTION; TUMOR MARKERS, BIOLOGICAL;

EID: 61949289044     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Golub, T., et al., Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 1999. 286: p. 531-537.
2. Schuetz, A., et al., Molecular classification of renal tumors by gene expression profiling. J Mol Diagn, 2004.
3. Singh, D., et al, Gene expression correlates of clinical prostate cancer behavior. Cancer Cell, 2002. 1: p. 203-209. (Pubitemid 41039166)
4. van't Veer, L., et al, Gene expression profiling predicts clinical outcome of breast cancer. Nature, 2002. 415: p. 530-536.
5. Braga-Neto, U. and E. Dougherty, Is cross-validation valid for smallsample microarray classification? Bioinfonnatics, 2004. 20: p. 374-380. (Pubitemid 38262769)
6. Chuaqui, R., et al, Post-analysis follow-up and validation of microarray experiments. Nature Genetics, 2002. 32: p. 509-514. (Pubitemid 35435154)
7. Morey, J., J. Ryna, and F. Van Dolah, Microarray validation: factors influencing correlation between oligonucleotide microarrays and real-time PCR. BioI. Proced. Online, 2006. 8(1): p. 175-193. (Pubitemid 44942143)
8. Shi, L., et al., The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol, 2006.24(9): p. 1151-61. (Pubitemid 44359727)
9. Stokes, T., et al., chip artifact CORRECTion (caCORRECT): A Bioinformatics System for Quality Assurance of Genomics and Proteomics Array Data. Annals of Biomedical Engineering, 2007. 35: p. 1068-1080. (Pubitemid 46828715)
10. Aerts, S., et al., Gene prioritization through genomic data fusion. Nature Biotechnology, 2006. 24(5): p. 537-544.
11. Kuffner, R., K. Fundel, and R. Zimmer, Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts. Bioinfonnatics, 2005. 21: p. ii259-ii267. (Pubitemid 41535459)
12. Kong, S., W. Pu, and P. Park, A multivariate approach for integrating genome-wide expression data and biological knowledge. Bioinfonnatics, 2006.22(19): p. 2373-2380. (Pubitemid 44566966)
13. Mukherjee, S. and S. Roberts, A theoretical analysis of the selection of differentially expressed genes. J Bioinfonnatics Comput BioI, 2005. 3: p. 627-643. (Pubitemid 41015282)
14. Bellazzi, R. and B. Zupan, Towards knowledge-based gene expression data mining. Journal of Biomedical Infonnatics, 2007. 40: p. 787-802. (Pubitemid 350088620)
15. Efron, B. and R. Tibshirani, Improvements on Cross-Validation: The. 632+ Bootstrap Method. Journal of the American Statistical Association, 1997. 92(438): p. 548-560.
16. Jones, J., et al., Gene signatures of progression and metastasis in renal cell cancer. Clin Cancer Res, 2005. 11(16): p. 5730-9. (Pubitemid 41170297)
17. Schrader, A., et al., CXCR4/CXCLJ 2 expression and signalling in kidney cancer. British Journal of Cancer, 2002. 86: p. 1250-1256.
18. Rosendahl, A. and G. Forseberg, IGF-I and IGFBP-3 augment transforming growth factor-beta actions in human renal carcinoma cells. Kidney International, 2006.70: p. 1584-1590. (Pubitemid 44606993)
19. Ivanov, S., et al., Two novel VHL targets, TGFBI (BIGH3) and its transactivator KLF 10, are up-regulated in renal clear cell carcinoma and other tumors. Biochem Biophys Res Commun, 2008.