Communications of the Korean Institute of Information Scientists and Engineers (정보과학회지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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바이오데이터 분석을 위한 기계학습 기술

  • Published : 2007.03.31
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Abstract

Keywords

References

  1. Hwang, K.-B., Cho, D.-Y., Park, S.-W., Kim, S.-D., and Zhang, B.-T., Applying machine learning techniques to analysis of gene expression data: cancer diagnosis, Methods of Microarray Data Analysis(Proceedings of CAMDA 2000), Lin, S.M. and Johnson, K.F.(eds.), pp. 167-182, Kluwer Academic Publichers, 2002
  2. Zhang, B.-T. and Hwang, K.-B., Bayesian network classifiers for gene expression analysis, A Practical Approach to Microarray Data Analysis, Berrar, D.P., Dubitzky, W., and Granzow, M.(eds.), pp. 150-165, Kluwer Academic Publishers, 2003
  3. Hwang, K.-B. and Zhang, B.-T., "Bayesian model averaging of Bayesian network classifiers over multiple node-orders: application to sparse datasets," IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 35(6):1302-1310, 2005 https://doi.org/10.1109/TSMCB.2005.850162
  4. Raval, A., Ghahramani, Z., and Wild, D.L., "A Bayesian network model for protein fold and remote homologue recognition," Bioinformatics, 18(6):788-801, 2002 https://doi.org/10.1093/bioinformatics/18.6.788
  5. Ben-Gal, I., Shani, A., Gohr, A., Grau, J., Arviv, S., Shmilovici, A., Posch, S., and Grosse, I., "Identification of transcription factor binding sites with variable-order Bayesian networks," Bioinformatics, 21(11): 2657- 2666, 2005 https://doi.org/10.1093/bioinformatics/bti410
  6. Troyanskaya O.G., Dolinski K., Owen A.B., Altman R.B., and Botstein D., "A Bayesian framework for combining heterogeneous data sources for gene function prediction(in S. cerevisiae)," Proc Natl Acad Sci,100(14): 8348-53, 2003
  7. Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., and Haussler, D., "Support vector machine classification and validation of cancer tissue samples using microarray expression data," Bioinformatics, 16(10):906-914, 2000 https://doi.org/10.1093/bioinformatics/16.10.906
  8. Leslie, C., Eskin, E. and Noble, W. S., "The spectrum kernel: A string kernel for SVM protein classification," Proceedings of the Pacific Symposium on Biocomputing, 564- 575, 2002
  9. Vert, J.-P., "A tree kernel to analyze phylogenetic profiles," Bioinformatics, 18: S276- S284, 2002 https://doi.org/10.1093/bioinformatics/18.suppl_1.S276
  10. Saigo, H., Vert, J.-P., Ueda, N., and Akutsu, T., "Protein homology detection using string alignment kernels," Bioinformatics, 20:1682- 1689, 2004. https://doi.org/10.1093/bioinformatics/bth141
  11. Kashima, H., Tsuda, K., and Inokuchi, A., "Marginalized Kernels Between Labeled Graphs," In Proc. 20th International Conference on Machine Learning(ICML 2003), Washington, DC USA, 2003
  12. Kim, S.-K., Nam, J.-W., Rhee, J.-K., Lee, W.-J., and Zhang, B.-T., "miTarget: microRNA target-gene prediction using a Support Vector Machine," BMC Bioinformatics, 7(1):411, 2006. https://doi.org/10.1186/1471-2105-7-411
  13. Hochreiter, S. and Obermayer, K., "Kernel Methods in Computational Biology, chapter Gene Selection for Microarray Data," 319- 356. Eds.: Scholkopf B., Tsuda, K. and Vert, J.-P., MIT Press, Cambridge, Massachusetts, 2004.
  14. Kim, B.H., Park, S.B., and Zhang, B.-T., "PromSearch: a hybrid approach to human core-promoter prediction," Lect. Notes Comput. SC., 3177:125-131, 2004
  15. Tavazoie, S., Hughes, J., Campbell, M., Cho, R.J., and Church, G.M., "Systematic determination of genetic network architecture," Nat. Genet., 22:281-285, 1999 https://doi.org/10.1038/10343
  16. Dembele, D. and Kastner, P., "Fuzzy cmeans method for clustering microarray data," Bioinformatics, 19: 973-980, 2003 https://doi.org/10.1093/bioinformatics/btg119
  17. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky. E., Lander, E.S., and Golub, T.R., "Interpreting patters of gene expression with self-organizing maps: methods and application to hematopoietic differentiation," PNAS, 96:2907-2912, 1999 https://doi.org/10.1073/pnas.96.6.2907
  18. Mahony, S., Golden, A., Smith, T.J., and Benos, P.V., "Improved detection of DNA motifs using a self- organized clustering of familial binding profiles," Bioinformatics, 21: i283-i291, 2005 https://doi.org/10.1093/bioinformatics/bti1025
  19. Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D., "Cluster analysis and display of genome-wide expression patterns," PNAS, 95:14863-14868, 1998 https://doi.org/10.1073/pnas.95.25.14863
  20. Jothi, R., Zotenko, E., Tasneem, A., and Przytycka, T. M., "COCO-CL: hierarchical clustering of homology relations based on evolutionary correlations," Bioinformatics, 22(7): 779-788, 2006 https://doi.org/10.1093/bioinformatics/btl009
  21. Qin, J., Lewis, D., and Noble,W., "Kernel hierarchical gene clustering from microarray gene expression data," Bioinformatics, 19: 2097-2104, 2003 https://doi.org/10.1093/bioinformatics/btg288
  22. Model, F., König, T., Piepenbrock, C., and Adorjan, P., "Statistical process control for large scale microarray experiments," Bioinformatics, 18:S155-S163, 2002 https://doi.org/10.1093/bioinformatics/18.suppl_1.S155
  23. Ji, Y., Wu, C., Liu, P., Wang, J., and Coombes, K.R., "Applications of beta-mixture models in bioinformatics," Bioinformatics, 21(9):2118-2122, 2005 https://doi.org/10.1093/bioinformatics/bti318
  24. Mayrose, I., Friedman, N., and Pupko, T., "A Gamma mixture model better accounts for among site rate heterogeneity," Bioinformatics, 21:ii151-ii158, 2005 https://doi.org/10.1093/bioinformatics/bti1125
  25. Madeira, S.C. and Oliveira, A.L., "Biclustering algorithms for biological data analysis: a survey," IEEE Transactions on Computational Biology and Bioinformatics, 1(1):24-45, 2004 https://doi.org/10.1109/TCBB.2004.2
  26. Friedman, N., Linial, M., Nachman, I., and Pe'er, D., "Using Bayesian Network to Analyze Expression Data," J. Computational Biology, 7:601-620, 2000 https://doi.org/10.1089/106652700750050961
  27. Perrin, B.-E., Ralaivola, L., Mazurie, A., et al., "Gene networks inference using dynamic Bayesian networks," Bioinformatics, 19:ii138- ii148, 2003 https://doi.org/10.1093/bioinformatics/btg1018
  28. Chang, J.-H., Hwang, K.-B., O, S.J., and Zhang, B.-T., "Bayesian network learning with feature abstraction for gene-drug dependency analysis," Journal of Bioinformatics and Computational Biology, 3(1):61-77, 2005 https://doi.org/10.1142/S0219720005000874
  29. Pournara, I.V. and Wernisch, L., "Reconstruction of gene networks using Bayesian learning and manipulation experiments," Bioinformatics, 20(17): 2934-2942, 2004 https://doi.org/10.1093/bioinformatics/bth337
  30. Werhli, A.V., Grzegorczyk, M., and Husmeier, D., "Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and Bayesian networks," Bioinformatics, 22(20):2523-2531, 2006 https://doi.org/10.1093/bioinformatics/btl391
  31. Segal, E., Wang, H., and Koller, D., "Discovering molecular pathways from protein interaction and gene expression data," Bioinformatics, 19 Supple 1:i264-71, 2003 https://doi.org/10.1093/bioinformatics/btg1037
  32. Nariai, N., Kim, S., Imoto, S., et al., "Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks," In Proceedings of the 9th Pacific Symposium on Biocomputing, 336-347, 2004
  33. Joung, J.-G., Shin, D., Seong, R.-H., and Zhang, B.-T., "Identification of Regulatory Modules by Co-clustering Latent Variable Models: Stem Cell Differentiation," Bioinformatics, 22(16): 2005-2011, 2006 https://doi.org/10.1093/bioinformatics/btl343
  34. Hwang, K.-B., Kim, B.-H., and Zhang, B.-T., "Learning hierarchical Bayesian networks for large-scale data analysis," Lect. Notes Comput. SC., 4232:670-679, 2006
  35. Zhang, B.-T, Yang, J., and Chi, S.W., "Self-organizing latent lattice models for temporal gene expression profiling," Mach. Learn., 52(1/2):67-89, 2003 https://doi.org/10.1023/A:1023993325417
  36. Whitney, A. R., Diehn, M., Popper, S. J., Alizadeh, A. A., Boldrick, J.C., Relman, D. A., and Brown, P. O., "Individuality and variation in gene expression patterns in human blood," PNAS, 100:1896-1901, 2003 https://doi.org/10.1073/pnas.252784499
  37. Borovecki, F., Lovrecic, L., Zhou, J., Jeong, H., Then, F., Rosas, H.D., Hersch, S.M., Hogarth, P., Bouzou, B., Jensen, R.V., and Krainc, D., "Genome-wide expression profiling of human blood reveals biomarkers for Huntington's disease," PNAS, 102(31):11023-8, 2005. https://doi.org/10.1073/pnas.0504921102
  38. 김병희, 김성천, 장병탁, 기계학습에 의한 압타머 칩 데이터 기반 심혈관 질환 단계의 예측, 한국컴퓨 터종합학술대회 2006 논문집, 제33권 1(A), pp. 85-87, 2006.06
  39. 엄재홍, 김병희, 이제근, 허민오, 박영진, 김민혁, 김성천, 장병탁, AptaCDSS-압타머칩을 이용한 심 혈관질환 질환단계 예측 및 진단의사결정지원시스템, 한국정보과학회 가을학술발표 논문집, 제33권 2(A), pp. 28-32, 2006
  40. 김병희, 김성천, 장병탁, Potential SVM을 이용한 압타머칩에서의 바이오마커 탐색, 한국정보과학회 가을학술발표 논문집, 제33권 2(A), pp. 22- 27, 2006
  41. Silva, J.M., et al., "Second-generation shRNA libraries covering the mouse and human genomes," Nat Genet, 37(11):1281-8, 2005 https://doi.org/10.1038/ng1650
  42. Chang, K., Elledge, S.J., and Hannon, G. J., "Lessons from Nature: microRNA-based shRNA libraries," Nat Methods, 3(9): 707- 14, 2006 https://doi.org/10.1038/nmeth923
  43. Saetrom, P., "Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming," Bioinformatics, 20(17):3055-63, 2004 https://doi.org/10.1093/bioinformatics/bth364
  44. Hayashita, Y., et al., "A polycistronic micro- RNA cluster, miR-17-92, is overexpressed in human lung cancers and enhances cell proliferation," Cancer Res, 65(21):9628-32, 2005 https://doi.org/10.1158/0008-5472.CAN-05-2352
  45. Eder, M. and Scherr, M., "MicroRNA and lung cancer," N Engl J Med, 352(23):2446- 8, 2005 https://doi.org/10.1056/NEJMcibr051201
  46. Esquela-Kerscher, A. and Slack, F.J., "Oncomirs-microRNAs with a role in cancer," Nat Rev Cancer, 6(4):259-69, 2006. https://doi.org/10.1038/nrc1840
  47. Gregory, R.I. and Shiekhattar, R., "MicroRNA biogenesis and cancer," Cancer Res, 65(9): 3509-12, 2005 https://doi.org/10.1158/0008-5472.CAN-05-0298
  48. Johnson, S.M., et al., "RAS is regulated by the let-7 microRNA family," Cell, 120(5): 635-47, 2005 https://doi.org/10.1016/j.cell.2005.01.014
  49. Croce, C.M. and Calin, G.A., "miRNAs, cancer, and stem cell division," Cell, 122(1): 6-7, 2005 https://doi.org/10.1016/j.cell.2005.06.036
  50. Huang, J.C., Morris, Q.D., and Frey, B.J., "Detecting microRNA targets by linking sequence, microRNA and gene expression data," in Tenth Annual International Conference on Research in Computational Molecular Biology(RECOMB). Venice, Italy, 2006
  51. Joung, J.-G., Hwang, K.-B., Nam, J.-W., Kim, S.-J., and Zhang, B.-T., "Discovery of microRNA-mRNA modules via populationbased probabilistic learning," Bioinformatics, 2007(in print)
  52. zur Hausen, H. "Papillomaviruses causing cancer: evasion from host-cell control in early events in carcinogenesis," Journal of National Cancer Inst., 92:690-698, 2000
  53. IARC Monographs on the Evaluation of the Carcinogenic Risks to Humans. Lyon, France: IARC Scientific Publications, 1995
  54. Janicek, M.F. and Averette, H.E., "Cervical cancer: prevention, diagnosis, and therapeutics," A Cancer Journal for Clinicians, 51, 92-114, 2001 https://doi.org/10.3322/canjclin.51.2.92
  55. Park, S.-B., Hwang, S., and Zhang, B.-T., "Mining the risk types of human papillomavirus( HPV) by AdaCost," Lecture Notes in Computer Science, 2736: 403-412, 2003
  56. Joung, J.-G., O, S.J., and Zhang, B.-T., "Protein sequence-based risk classification for human papillomaviruses," Computers in Biology and Medicine, 36:656-667, 2006 https://doi.org/10.1016/j.compbiomed.2004.04.007