Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

Hypernetwork Classifiers for Microarray-Based miRNA Module Analysis

마이크로어레이 기반 miRNA 모듈 분석을 위한 하이퍼망 분류 기법

  • 김선 (서울대학교 컴퓨터공학부) ;
  • 김수진 (서울대학교 생물정보학 협동과정) ;
  • 장병탁 (서울대학교 컴퓨터공학부)
  • Published : 2008.06.15
Download PDF
⟨ Previous Next ⟩

Abstract

High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.

마이크로어레이는 분자 생물학 실험에 있어 중요한 도구로 사용되고 있으며, 마이크로어레이 데이타 분석을 위한 다양한 계산학적 방법이 개발되어 왔다. 그러나, 기존 분석방법은 주어진 조건에 영향을 주는 개별 유전자를 추출하는 데 강한 방면, 유전자 간의 복합작용에 의한 영향을 분석하기 힘들다는 단점을 가지고 있다. 하이퍼망 모델은 생물학적인 네트워크 작용을 모방한 구조이며, 계산과정에서 요소간의 복합작용을 직접 고려하기 때문에 기존 방법에서 다루기 힘들었던 요소간 상호작용 분석이 가능하다는 장점을 가진다. 본 논문에서는 마이크로어레이 데이타를 기반으로 microRNA(miRNA) 프로파일 분석을 위한 하이퍼망 분류 기법을 소개한다. 하이퍼망 분류기는 miRNA 쌍을 기본 요소로 하여 진화 과정을 통해 miRNA 분류 데이타를 학습한다. 학습된 하이퍼망으로부터 유의하다.고 판단되는 miRNA 모듈을 쉽게 추출할 수 있으며, 사용자는 추출된 모듈의 유치미성을 직접 판단할 수 있다. 하이퍼망 분류기는 암 관련 miRNA 발현 데이타 분류 실험을 통해 91.46%의 정확도를 보임으로써 기존 기계학습 방법에 비해 뛰어난 성능을 보여주었으며, 하이퍼망 분석을 통해 생물학적으로 유의한 miRNA 모듈을 찾을 수 있음을 확인하였다.

Keywords

References

  1. Ramaswamy, S. and Golub, T.R., 'DNA Microarrays in Clinical Oncology,' Journal of Clinical Oncology, Vol.20, pp. 1932-1941, 2002 https://doi.org/10.1200/JCO.2002.20.7.1932
  2. Segal, E., Friedman, N., Kaminski, N., Regev, A., and Koller, D., 'From Signatures to Models: Understanding Cancer Using Microarrays,' Nature Genetics, Vol.37, s38-s45, 2005 https://doi.org/10.1038/ng1561
  3. Segal, E., Friedman, N., Koller, D., and Regev, A., 'A Module Map Showing Conditional Activity of Expression Modules in Cancer,' Nature Genetics, Vol.36, pp. 1090-1098, 2004 https://doi.org/10.1038/ng1434
  4. Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet C.W., Furey, T.S., Ares, M., Jr., and Haussler, D., 'Knowledge-Based Analysis of Microarray Gene Expression Data by Using Support Vector Machines,' Proceedings of the National Academy of Sciences, Vol.97, No.1, pp. 262-267, 2000 https://doi.org/10.1073/pnas.97.1.262
  5. Dettling, M. and Buhlmann, P., 'Boosting for Tumor Classification with Gene Expression Data,' Bioinformatics, Vol.19, pp. 1061-1069, 2003 https://doi.org/10.1093/bioinformatics/btf867
  6. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., and Mesirov, J.P., 'Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles,' Proceedings of the National Academy of Sciences, Vol.102, pp. 15545-15550, 2005
  7. Huang, E., Ishida, S., Pittman, J., Dressman, H., Bild, A., Kloos, M., Kloos, M., Pestell, R.G., West, M., and Nevins, J.R., 'Gene Expression Phenotypic Models That Predict the Activity of Oncogenic Pathways,' Nature Genetics, Vol.34, pp. 226-230, 2003 https://doi.org/10.1038/ng1167
  8. Meltzer, P.S., 'Cancer Genomics: Small RNAs with Big Impacts,' Nature, Vol.435, pp. 745-746, 2005 https://doi.org/10.1038/435745a
  9. Zhang, B.-T., 'Random Hypergraph Models of Learning and Memory in Biomolecular Networks: Shorter-Term Adaptability vs. Longer-Term Persistency,' IEEE Symposium on Foundations of Computational Intelligence, pp. 344-349, 2007
  10. Kim, S., Kim, S.-J., and Zhang, B.-T., 'Evolving Hypernetwork Classifiers for microRNA Expression Profile Analysis,' IEEE Congress on Evolutionary Computation, pp. 313-319, 2007
  11. Berge, C., Graphs and Hypergraphs, North-Holland Publishing, 1973
  12. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashitan, N., Chklovskii, D., and Alon, U., 'Network Motifs: Simple Building Blocks of Comples Networks,' Science, Vol.298, pp. 824-827, 2002 https://doi.org/10.1126/science.298.5594.824
  13. MacKay, D., Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2004
  14. Kim, S., Heo, M.-O., and Zhang, B.-T., 'Text Classifiers Evolved on a Simulated DNA Computer,' IEEE Congress on Evolutionary Computation, pp. 9196-9202, 2006
  15. Lu, J., Getz, G., Miska, E.A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B.L., Mak, R.H., Ferrando, A.A., Downing, J.R., Jacks, T., Horvitz, H.R., and Golub, T.R., 'MicroRNA Expression Profiles Classify Human Cancers,' Nature, Vol.435, pp. 834-838, 2005 https://doi.org/10.1038/nature03702
  16. Calin, G.A., Sevignani, C., Dumitru, C.D., Hyslop, T., Noch, E., Yendamuri, S., Shimizu, M., Rattan, S., Bullrich, F., Negrini, M., and Croce, C.M., 'Human microRNA Genes are Frequently Located at Fragile Sites and Genomic Regions Involved in Cancers,' Proceedings of the National Academy of Sciences, Vol.101, No.9, pp. 2999-3004, 2006 https://doi.org/10.1073/pnas.0307323101
  17. Zhang, L., Huang, J., Yang, N., Greshock, J., Megraw, M.S., Giannakakis, A., Liang, S., Naylor, T.L., Barchetti, A., Ward, M.R., Yao, G., Medina, A., Brien-Jenkins, A.O., Katsaros, D., Hatzigeorgiou, A., Gimotty, P.A., Weber, B.L., and Coukos, G., 'MicroRNAs Exhibit High Frequency Genomic Alterations in Human Cancer,' Proceedings of the National Academy of Sciences, Vol.103, pp. 9136-9141, 2006
  18. Beissbarth, T., Speed, T.P., 'GOstat: Find Statistically Overrepresented Gene Ontologies within a Group of Genes,' Bioinformatics, Vol.20, No.9, pp. 1464-1465, 2004 https://doi.org/10.1093/bioinformatics/bth088
  19. Kashatus, D., Cogswell, P., and Baldwin, A.S., 'Expression of the Bcl-3 Proto-Oncogene Suppresses p53 Activation,' Genes and Development, Vol.20, pp. 225-235, 2006 https://doi.org/10.1101/gad.1352206
  20. Phan, R.T. and Dalla-Favera, R., 'The BCL6 Proto-Oncogene Suppresses p53 Expression in Germinal-Centre B Cells,' Nature, Vol.432, pp. 635-639, 2004 https://doi.org/10.1038/nature03147