MiRNA and gene expression based cancer classification using self-learning and co-training approaches

Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013

Volumn , Issue , 2013, Pages 495-498

  Ibrahim, Rania ^a   Yousri, Noha A ^a,b   Ismail, Mohamed A ^a   El Makky, Nagwa M ^a  

^a Alexandria University The Faculty Of Engineering   (Egypt)

^b EGYPT JAPAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Egypt)

Author keywords

Cancer sample classifiers; Co Training; miRNA and gene expression analysis; Self Learning; Semi supervised Approaches

Indexed keywords

EID: 84894520755     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBM.2013.6732544     Document Type: Conference Paper

References (25)

1. Y. Katayama, M. Maeda, K. Miyaguchi, S. Nemoto, M. Yasen, S. Tanaka, H. Mizushima, Y. Fukuoka, S. Arii and H. Tanaka. Identification of pathogenesis-related microRNAs in hepatocellular carcinoma by expression profiling. Oncol. Lett. 4(4): 817-823. October 2012.
2. S. Volinia, G. Calin and C. Liu. A microRNA expression signature of human solid tumors defines cancer gene targets. Proceedings Natl. Acad. Sci. USA, 103:2257-2261, 2006.
3. K. Kim and S. Cho. Exploring Features and Classifiers to Classify MicroRNA Expression Profiles of Human Cancer. International Conference on Neural Information Processing (ICONIP), 2(10):234-241, 2010.
4. Y. Wang and M. H. Dunham. Classifier fusion for poorlydifferentiated tumor classification using both messenger RNA and microRNA expression profiles. In Proceedings of the 2006 Computational Systems Bioinformatics Conference (CSB 2006), Stanford, California, 2006.
5. Y. Zheng and C. Keong Kwoh. Cancer Classifcation with MicroRNA Expression Patterns Found By An Information Theory Approach. Journal of Computers (JCP), 1(5):30-39, 2006.
6. N. Du, K. Li, S. D. Mahajan, S. A. Schwartz, B. B. Nair, C. Bin Hsiao, A. Zhang. Gene Co-Adaboost: a semi-supervised approach for classifying gene expression data. Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine (BCB), pp. 531-535, USA, 2011.
7. O. Okun and H. Priisalu. Random Forest for Gene Expression Based Cancer Classification: Overlooked Issues. Pattern Recognition and Image Analysis Volume 4478, pp. 483-490, 2007.
8. M. Klassen, M. Cummings and G. Saldana. Investigation of random forest performance with cancer microarray data. In proceedings of the ISCA 23rd International Conference on Computers and Their Applications, CATA 2008, April 9-11, 2008, Cancun, Mexico, pp. 64-69, ISCA, 2008.
9. H. Chen, B. Yang, J. Liu and D. Liu. MicroRNA Signatures Predict Oestrogen Receptor, Progesterone Receptor and HER2/neu Receptor Status in Breast Cancer. Expert Syst. Appl. 38:9014-22, 2011.
10. R. Upstill-Goddard, D. Eccles, S. Ennis, S. Rafiq, W. Tapper, J. Fliege and A. Collins. Support Vector Machine Classifier for Estrogen Receptor Positive and Negative Early-Onset Breast Cancer. PLoS ONE 8(7): e68606, 2013.
11. C. Rosenberg, M. Hebert and H. Schneiderman. Semi-Supervised Self-Training of Object Detection Models. 7th IEEE Workshop on Applications of Computer Vision/IEEE Workshop on Motion and Video Computing (WACV/MOTION), pp. 29-36, Breckenridge, CO, USA, 2005.
12. R. Mihalcea. Co-training and Self-training for Word Sence Disambiguation. In Proceedings of CoNLL, pp. 33-40, Boston, MA, USA, 2004.
13. J. Weston. Semi-supervised protein classification using cluster kernels. Bioinformatics, 21, 3241-3247, 2005.
14. J. Ernst. Semi-supervised method for predicting transcription factorgene interactions in Escherichia coli. Pols Comput. Biol., 4(3), e1000044, 2008.
15. M. Shi and B. Zhang. Semi-supervised learning improves gene expression-based prediction of cancer recurrence. Bioinformatics, 27(21):3017-3023, 2011.
16. O. Chapelle, B. Schölkopf and A. Zien. Semi-supervised learning. Cambridge, Mass., MIT Press, 2006.
17. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. Proceedings of the Workshop on Computational Learning Theory (COLT), pp. 92-100, Wisconsin, USA, 1998.
18. T. Mitchell. The role of unlabeled data in supervised learning. Proceedings of the Sixth International Colloquium on Cognitive Science (ICCS), San Sebastian, Spain, 1999.
19. R. Mihalcea. Co-training and Self-training for Word Sense Disambiguation. In Proceedings of the Conference on Natural Language Learning (CoNLL), pp. 33-40, Boston, USA, May 2004.
20. S. Kiritchenko and S. Matwin. Email Classification with Co-Training. In Proceedings of Conference of the Center for Advanced Studies on Colleborative Research (CASCON), pp. 301-312, 2011.
21. B. John, AJ. Enright, A. Aravin, T. Tuschl, C. Sander and D. Marks. miRanda application: Human MicroRNA targets. PLoS Biol. Jul;3(7):e264, 2005.
22. L. Breiman. Random Foresets. Machine Learning, 45(1):5-32, 2001.
23. http://www.cs.waikato.ac.nz/ml/weka
24. http://olivier.chapelle.cc/lds
25. http://www.ncbi.nlm.nih.gov/geo