Interactive semisupervised learning for microarray analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 4, Issue 2, 2007, Pages 190-202

  Lu, Yijuan ^a   Tian, Qi ^a   Liu, Feng ^b   Sanchez, Maribel ^c   Wang, Yufeng ^c  

^a University of Texas at San Antonio   (United States)

^b UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO   (United States)

^c UNIVERSITY OF TEXAS AT SAN ANTONIO   (United States)

Author keywords

Kernel DEM; Microarray analysis; Relevance feedback; Semisupervised learning

Indexed keywords

LINEAR ALGORITHMS; MICROARRAY ANALYSIS; RELEVANCE FEEDBACK; SEMISUPERVISED LEARNING;

CELLS; DATA REDUCTION; DATA STRUCTURES; EXPERT SYSTEMS; GENE EXPRESSION; MICROARRAYS;

LEARNING SYSTEMS;

PLASMODIUM FALCIPARUM;

PROTEOME;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER INTERFACE; DISCRIMINANT ANALYSIS; DNA MICROARRAY; EVALUATION; GENE EXPRESSION PROFILING; METABOLISM; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DISCRIMINANT ANALYSIS; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; PROTEOME; USER-COMPUTER INTERFACE;

EID: 34248344212     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2007.070206     Document Type: Article

References (46)

1. G. Salton and M.J. McGill, Introduction to Modern Information Retrieval. McGraw-Hill, 1992.
2. Y. Rui, T.S. Huang, M. Ortega, and S. Mehrotra, "Relevance Feedback: A Power Tool in Interactive Content-Based Image Retrieval," IEEE Trans. Circuits and Systems for Video Technology, vol. 8, no. 5, pp. 644-655, Sept. 1998.
3. X. Zhou and T.S. Huang, "Relevance Feedback in Image Retrieval: A Comprehensive Review," ACM Multimedia Systems J., vol. 8, no. 6, pp. 536-544, 2003.
4. S. Dudoit, J. Fridlyand, and T.P. Speed, "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Technical Report 576, 2000.
5. L. Li, C.R. Weinberg, T.A. Darden, and L.G. Pedersen, "Gene Selection for Sample Classification Based on Gene Expression Data: Study of Sensitivity to Choice of Parameters of the GA/KNN Method," Bioinformatics, vol. 17, no. 12, pp. 1131-1142, 2001.
6. J. Khan et al., "Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and Artificial Neural Networks," Nature Medicine, vol. 7, no. 6, pp. 673-679, 2001.
7. M.P.S. Brown et al., "Knowledge-Based Analysis of Microarray Gene Expression Data by Using Support Vector Machines," Proc. Nat'l Academy of Sciences USA, pp. 262-267, 2000.
8. Y. Wu, Q. Tian, and T.S. Huang, "Discriminant EM Algorithm with Application to Image Retrieval," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000.
9. A. Blum and T. Mitchell, "Combining Labeled and Unlabeled Data with Co-Training," Proc. Workshop Computational Learning Theory (COLT), pp. 92-100, 1998.
10. O. Chapelle, B. Schölkopf, and A. Zien, SemiSupervised Learning. MIT Press, 2006.
11. T.M. Huang, V. Kecman, and I. Kopriva, Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semisupervised and Unsupervised Learning, p. 96. Springer-Verlag, 2006.
12. X. Zhu, "Semisupervised Learning Literature Survey," http://www.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf, 2006.
13. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, second ed. John Wiley and Sons, 2001.
14. K.R. Muller et al., "An Introduction to Kernel-Based Learning Algorithms," IEEE Trans. Neural Networks, vol. 12, no. 2, Mar. 2001.
15. V. Vapnik, The Nature of Statistical Learning Theory, second ed., 2000.
16. B. Schölkopf, A. Smola, and K.R. Müller, "Nonlinear Component Analysis as a Kernel Eigenvalue Problem," Neural Computation, vol. 10, pp. 1299-1319, 1998.
17. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A. Smola, and K. Müller, "Fisher Discriminant Analysis with Kernels," Proc. IEEE Workshop Neural Networks for Signal Processing, 1999.
18. B. Schölkopf and A.J. Smola, Learning with Kernels. MIT Press, 2002.
19. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A. Smola, and K. Müller, "Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, May 2003.
20. Q. Tian, J. Yu, Q. Xue, and N. Sebe, "A New Analysis of the Value of Unlabeled Data in Semisupervised Learning for Image Retrieval," Proc. Int'l Conf. Multimedia and Expo (ICME '04), June 2004.
21. T. Kurita and T. Kato, "Learning of Personal Visual Impression for Image Database Systems," Proc. Int'l Conf. Document Analysis and Recognition, 1993.
22. R.W. Picard, T.P. Minka, and M. Szummer, "Modeling User Subjectivity in Image Libraries," Proc. Int'l Conf. Image Processing, 1996.
23. Y. Rui, T.S. Huang, M. Ortega, and S. Mehrotra, "Relevance Feedback: A Power Tool in Interactive Content-Based Image Retrieval," IEEE Trans Circuits and Systems for Video Technology, vol. 8, no. 5, pp. 644-655, 1998.
24. J. Laakaonen, M. Koskela, and E. Oja, "PicSOM: Self-Organizing Maps for Content-Based Image Retrieval," Proc. IEEE Int'l Conf. Neural Networks, 1999.
25. Y. Rui and T.S. Huang, "Optimizing Learning in Image Retrieval," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 236-243, 2000.
26. Y. Ishikawa, R. Subramanya, and C. Faloutsos, "MindReader: Query Database through Multiple Examples," Proc. 24th VLDB Conf., 1998.
27. I.J. Cox, M.L. Miller, T.P. Minka, and T.V. Papsthomas, "The Bayesian Image Retrieval System, Pichunter: Theory, Implementation, and Pychophysical Experiments," IEEE Trans. Image Processing, vol. 9, no. 1, pp. 20-37, 2000.
28. X. Zhou and T.S. Huang, "Small Sample Learning During Multimedia Retrieval Using biasMap," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001.
29. S. Tong and E. Chang, "Support Vector Machine Active Learning for Image Retrieval," Proc. ACM Int'l Conf. Multimedia, pp. 107-118, 2001.
30. L. Raton, O. Maron, W.E.L. Crimson, and T. Lozano-Pérez, "A Framework for Learning Query Concepts in Image Classification," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 1999.
31. Z. Stejic, Y. Takama, and K. Hirota, "Genetic Algorithm-Based Relevance Feedback for Image Retrieval Using Local Similarity Patterns," Information Processing and Management, vol. 39, no. 1, pp. 1-23, 2003.
32. M. Ashburner et al., "Gene Ontology: Tool for the Unification of Biology," The Gene Ontology Consortium. Nature Genetics, vol. 25, pp. 25-29, 2000.
33. M.B. Eisen, P.T. Spellman, P.O. Brown, and D. Botstein, "Cluster Analysis and Display of Genome-Wide Expression Patterns," Proc. Nat'l Academy of Sciences USA, vol. 95, no. 25, pp. 14863-14868, 1998.
34. S. Ng, S. Tan, and V.S. Sundararajan, "On Combining Multiple Microarray Studies for Improved Functional Classification by Whole-Dataset Feature Selection," Genome Informatics, vol. 14, pp. 44-53, 2003.
35. M.P. Brown et al., "Knowledge-Based Analysis of Microarray Gene Expression Data by Using Support Vector Machines," Proc. Nat'l Academy of Sciences USA, vol. 97, no. 1, pp. 262-267, 2000.
36. A. Mateos et al., "Systematic Learning of Gene Functional Classes from DNA Array Expression Data by Using Multiplayer Perceptrons," Genomes Research, vol. 12, no. 11, pp. 1703-1715, 2002.
37. C. van Rijsbergen, Information Retrieval, second ed. Butterworths, 1979.
38. M.J. Gardner et al., "Genome Sequence of the Human Malaria Parasite Plasmodium Falciparum," Nature, vol. 419, pp. 498-511, 2002.
39. Z. Bozdech, M. Llinas, B.L. Pulliam, E.D. Wong, J. Zhu, and J.L. DeRisi, "The Transcriptome of the Intraerythrocytic Development Cycle of Plasmodium Falciparum," Plos Biology, vol. 1, no. 1, pp. 1-16, 2003.
40. S. Tong and E. Chang, "Support Vector Machine Active Learning for Image Retrieval," Proc. ACM Int'l Conf. Multimedia, pp. 107-118, Oct. 2001.
41. The Gene Ontology Consortium, "Gene Ontology: Tool for the Unification of Biology," Nature Genetics, vol. 25, pp. 25-29, 2000.
42. Y. Wu, X. Wang, X. Liu, and Y. Wang, "Data-Mining Approaches Reveal Hidden Families of Proteases in the Genome of Malaria Parasite," Genome Research, vol. 13, pp. 601-616, 2003.
43. S.M. Gantt, J.M. Myung, M.R. Briones, W.D. Li, E.J. Corey, S. Omura, V. Nussenzweig, and P. Sinnis, "Proteasome Inhibitors Block Development of Plasmodium spp.," Antimicrobial Agents in Chemotherapy, vol. 42, pp. 2731-2738, 1998.
44. H. Kitano, "Systems Biology: A Brief Overview," Science, vol. 295, pp. 1662-1664, 2002.
45. P.M. Bowers, S.J. Cokus, D. Eisenberg, and T.O. Yeates, "Use of Logic Relationships to Decipher Protein Network Organization," Science, vol. 306, pp. 2246-2249, 2004.
46. R. Sharan, S. Suthram, R.M. Kelley, T. Kuhn, S. McCuine, P. Uetz, T. Sittler, R.M. Karp, and T. Ideker, "Conserved Patterns of Protein Interaction in Multiple Species," Proc Nat'l Academy of Sciences USA, vol. 102, pp. 1974-1979, 2005.