Bayesian unsupervised learning with multiple data types

Statistical Applications in Genetics and Molecular Biology

Volumn 8, Issue 1, 2009, Pages

  Agius, Phaedra ^a   Ying, Yiming ^b   Campbell, Colin ^b  

^a MSKCC ^*  (United States)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

Bayesian learning; Breast cancer; Cancer subtypes; Clusters; Correspondence model; Genes; MicroRNA; Multiple datasets; Unsupervised learning

Indexed keywords

HEPATOCYTE NUCLEAR FACTOR 3ALPHA; MICRORNA; TRANSCRIPTION FACTOR GATA 3; X BOX BINDING PROTEIN 1;

ARTICLE; BAYES THEOREM; BAYESIAN LEARNING; BREAST CANCER; CLUSTER ANALYSIS; ESTROGEN RESPONSIVE ELEMENT; GENE AMPLIFICATION; GENE EXPRESSION; HUMAN; VARIANCE; BIOLOGICAL MODEL; BIOLOGY; CLASSIFICATION; GENE EXPRESSION PROFILING; METABOLISM; NEOPLASM; REVIEW; STATISTICS;

BAYES THEOREM; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; HUMANS; MODELS, BIOLOGICAL; NEOPLASMS;

EID: 68249123199     PISSN: None     EISSN: 15446115     Source Type: Journal    
DOI: 10.2202/1544-6115.1441     Document Type: Article

References (37)

1. H Attias. Inferring parameters and structure of latent variable models by variational Bayes. In Proceedings of the 15th Conference on Uncertainty in Artifical Intelligence, pages 21-30. Morgan-Kaufmann, San Francisco, CA, 1999.
2. H Attias. A variational bayesian framework for graphical models. Advances in Neural Information Processing Systems 12, pages 209-215, 2000.
3. M J Beal and Z Ghahramani. The variational bayesian em algorithm for incomplete data: with application to scoring graphical model structures. Bayesian Statistics, 7:453-464, 2003.
4. J Bi, T Zhang, and K Bennett. Column-generation boosting mthods for mixture of kernels. In Proceedings of SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 521-526, 2004.
5. David M. Blei and Michael I. Jordan. Modeling annotated data. In SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 127-134, New York, NY, USA, 2003. ACM Press.
6. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
7. C Blenkiron et al. MicroRNA expression profiling of human breast cancer identifies new markers of tumour subtype. Genome Biology, 8:R214.1-R214.16, 2007.
8. R M Boggs, Z M Wright, M J Stickney, W W Porter, and K E Murphy. MicroRNA expression in canine mammary cancer. Mamm. Genome, 19:561-9, 2008.
9. L Carrivick. Probabilistic models in the biomedical sciences. PhD Thesis, Department of Engineering Mathematics, University of Bristol, UK, 2006.
10. L. Carrivick, S. Rogers, J. Clark, C. Campbell, M. Girolami, and C. Cooper. Identification of prognostic signatures in breast cancer microarray data using bayesian techniques. Journal of the Royal Society: Interface, 3:367-381, 2006.
11. J Carroll et al. Chromosome-wide mapping of estrogen receptor binding reveals long-range regulation requiring the forkhead protein FOXA1. Cell, 122:33-43, 2005.
12. A Dobra, B Jones, C Hans, J Nevins, and M West. Sparse graphical models for exploring gene expression data. Journal of Multivariate Analysis, 90:196-212, 2004.
13. A Dobra and M West. Graphical model-based gene clustering and meta-gene expression analysis. Technical report, Duke University Technical Report, Statistics, 2004.
14. P Farmer et al. Identification of molecular apocrine breast tumours by microarray analysis. Oncogene, 24:4660-4671, 2005.
15. M Girolami and M Zhong. Data integration for classification problems employing gaussian process priors. In Twentieth Annual Conference on Neural Information Processing Systems, pages 465-472, 2006.
16. Mark Girolami and Simon Rogers. Hierarchic bayesian models for kernel learning. In ICML: 22nd International Conference on Machine Learning, Bonn, Germany., August 2005.
17. Y Hua et al. MicroRNA expression profiling in human ovarian cancer: miR-214 induces cell survival and cisplatin resistance by targeting PTEN. Cancer Research, 68:425-433, 2008.
18. M Jordan, Z Ghahramani, T Jaakola, and L Saul. An introduction to variational methods for graphical models. Machine Learning, 37:183-233, 1999.
19. M Lacroix and G Leclerq. About GATA3, HNF3A and XBP1, three genes co-expressed with the oestrogen receptor-alpha gene (ESR1) in breast cancer. Molecular and Cellular Endocrinology, 219:1-7, 2004.
20. J Laganiere et al. Location analysis of estrogen receptor α target promoters reveals that FOXA1 defines a domain of the estrogen response. Proceedings National Academy Sciences, 102:11651-11656, 2005.
21. G R G Lanckriet, T De Bie, N Cristianini, M I Jordan, and W S Noble. A statistical framework for genomic data fusion. Bioinformatics, 20:2626-2635, 2004.
22. Y S Lee and A Dutta. The tumor suppressor microRNA let-7 represses the HMGA2 oncogene. Genes and Development, 21:1025-1030, 2007.
23. U Lehmann, B Hasemeier, D Romermann, M Muller, F Langer, and H Kreipe. Epigenetic inactivation of microRNA genes in mammary carcinoma. Verh. Dtsch. Ges. Pathol., 91:214-20, 2007.
24. M Middendorf, A Kundaje, C Wiggins, Y Freund, and C Leslie. Predicting genetic regulatory response using classification. In Proceedings of the Twelfth International Conference on Intelligent Systems in Molecular Biology (ISMB 2004), page in press, 2004.
25. Y Pawitan et al. Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Research, 7:R953-R964, 2005.
26. S Rogers, M Girolami, C Campbell, and R Breitling. The latent process decomposition of cDNA microarray datasets. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2:143-156, 2005.
27. L F Sempere, M Christensen, A Silahtaroglu, M Bak, C V Heath, G Schwartz, W Wells, S Kauppinen, and C N Cole. Altered microRNA expression confined to specific epithelial cell subpopulations in breast cancer. Cancer Research, 67:11612-20, 2007.
28. M-L Si et al. miR-21-mediated tumor growth. Oncogene, 26:2799-2803, 2007.
29. S Sonnenburg et al. Large scale multiple kernel learning. Journal of Machine Learning Research, 7:1531-1565, 2006.
30. T Sorlie et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proceedings National Academy Sciences, 98:10869-10874, 2001.
31. S F Tavazoie, C Alarcon, T Oskarsson, D Padua, Q Wang, P D Bos, W L Gerald, and J Massague. Endogenous human microRNAs that suppress breast cancer metastasis. Nature, 451:147-52, 2008.
32. A Teschendorff et al. A variational bayesian mixture modelling framework for cluster analysis of gene-expression data. Bioinformatics, 21:3025-3033, 2005.
33. L van 't Veer et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415:530-535, 2002.
34. Y Wang et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. The Lancet, 365:671-679, 2005.
35. M West et al. Predicting the clinical status of human breast cancer using gene expression profiles. Proceedings of the National Academy of Sciences, 98:11462-11467, 2001.
36. F Yang et al. Laser microdissection and microarray analysis of breast tumors reveal ER-α related genes and pathways. Oncogene, 25:1413-1419, 2006.
37. J Zhang, Y Y Du, Y F Lin, Y T Chen, L Yang, H J Wang, and D Ma. The cell growth suppressor, mir-126, targets IRS-1. Biochem Biophys Res Commun., 377:136-40, 2008.