Learning on weighted hypergraphs to integrate protein interactions and gene expressions for cancer outcome prediction

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2008, Pages 293-302

  Hwang, Taehyun ^a   Tian, Ze ^a   Kuang, Rui ^a   Kocher, Jean Pierre ^b  

^a UNIVERSITY OF MINNESOTA   (United States)

^b Mayo Clinic   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL PRINCIPLES; BIOMARKER IDENTIFICATION; BREAST CANCER; CANCER GENES; CANCER TREATMENT; DATA INTEGRATION; GENOMIC DATA; HYPERGRAPH; INTERACTING GENES; INTERACTION NETWORKS; MARKER GENES; MICROARRAY GENE EXPRESSION; OPTIMAL WEIGHTING; OUTCOME PREDICTION; PREDICTIVE MODELS; PRIOR KNOWLEDGE; PROTEIN INTERACTION; PROTEIN-PROTEIN INTERACTION NETWORKS; PROTEIN-PROTEIN INTERACTIONS; WEIGHTED HYPERGRAPHS;

ACTIVE NETWORKS; BIOACTIVITY; DATA HANDLING; EDUCATION; GENE EXPRESSION; GRAPH THEORY; INFORMATION MANAGEMENT; MINING;

LEARNING ALGORITHMS;

EID: 67049160152     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2008.37     Document Type: Conference Paper

References (22)

1. S. Agarwal, K. Branson, and S. Belongie. Higher order learning with graphs. In ICML 06: Proceedings of the 23rd international conference on Machine learning, pages 17-24, 2006.
2. S. Agarwal, J. Lim, L. Zelnik-Manor, P. Perona, D. Kriegman, and S. Belongie. Beyond pairwise clustering. In CVPR '05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume2, pages 838-845. IEEE Computer Society, 2005.
3. R. Aragues, C. Sander, and B. Oliva. Predicting cancer involvement of genes from heterogeneous data. BMC Bioinformatics, 9(172), 2008.
4. H. Y. Chuang, E. Lee, Y. T. Liu, D. Lee, and T. Ideker. Network-based classification of breast cancer metastasis. Mol Syst Biol, 3, 2007.
5. R. W. D. Hanahan. The hallmarks of cancer. Cell, 100:57-70, Jan 2000.
6. A. Dupuy and R. M. Simon. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst., 99(2):147-157, 2007.
7. G. V. Glinsky, A. B. Glinskii, A. J. Stephenson, R. M. Hoffman, and W. L. Gerald. Gene expression profiling predicts clinical outcome of prostate cancer. J. Clin. Invest., 113:913-923, 2004. (Pubitemid 38544150)
8. K. I. Goh et al. The human disease network. Proceeding National Academic Science USA, 104(21):8685-8690, 2007.
9. M. Gribskov and N. L. Robinson. Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Computers and Chemistry, 20(1):25-33, 1996. (Pubitemid 126405349)
10. P. Helmbold, J. Haerting, H. Klbl, D. B. Kopans, I. H. Kunkler, D. F. Ransohoff, M. J. van de Vijver, Y. D. He, L. J. van't Veer, and R. Bernards. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med, 347:1999-2009, 2002. (Pubitemid 35461656)
11. A. R. Mendelsohn and R. Brent. Protein interaction methods-toward an endgame. Science, (284):1948-1950, 1999.
12. M. Oti and H. Brunner. The modular nature of genetic diseases. Clinical Genetics, 71:1-11, 2007. (Pubitemid 44921616)
13. F. Rapaport, A. Zinovyev, M. Dutreix, E. Barillot, and J. P. Vert. Classification of microarray data using gene networks. BMC Bioinformatics, (35), 2007.
14. Y. Saad. Iterative Methods for Sparse Linear Systems. PWS Publishing Company, Boston, MA, 1996.
15. C. Sotiriou et al. Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis. Journal of the National Cancer Institute, 98:262-272, 2006. (Pubitemid 43264710)
16. Z. Sun and P. Yang. Gene expression profiling on lung cancer outcome prediction: Present clinical value and future premise. Cancer Epidemiology Biomarkers and Prevention, pages 2063-2068, 2006. (Pubitemid 44877027)
17. K. Tsuda. Propagating distributions on a hypergraph by dual information regularization. In ICML 05: Proceedings of the 22nd international conference on Machine learning, pages 920-927. ACM, 2005.
18. L. J. van't Veer, H. Dai, M. J. van de Vijver, S. H. Friend, and etc. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415:530-536, 2002. (Pubitemid 34130608)
19. Y.Wang, J. Klijn, Y. Zhang, A. Sieuwerts, M. Look, F. Yang, D. Talantov, M. Timmermans, M. M. van Gelder, and J. Yu. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. The Lancet, 365:671-679, 2005. (Pubitemid 40260888)
20. H. Xiong, X. He, C. Ding, Y. Zhang, V. Kumar, and S. R. Holbrook. Identification of functional modules in protein complexes via hyperclique pattern discovery. In PSB 05: Proceedings of the 2005 Pacific Symposium on Biocomputing, pages 221-232, 2005.
21. H. Xiong, P. N. Tan, and V. Kumar. Mining strong affinity association patterns in data sets with skewed support distribution. In ICDM 03: Proceedings of the Third IEEE International Conference on Data Mining, pages 387-394, Melbourne, Florida, USA, 2003.
22. D. Zhou, J. Huang, and B. Schölkopf. Learning with hypergraphs: Clustering, classification, and embedding. NIPS, 19:1633-1640, 2006.