Bayesian network learning with feature abstraction for gene-drug dependency analysis

Journal of Bioinformatics and Computational Biology

Volumn 3, Issue 1, 2005, Pages 61-77

  Chang, Jeong Ho ^a   Hwang, Kyu Baek ^a   Oh, S June ^b   Zhang, Byoung Tak ^a  

^a Seoul National University   (South Korea)

^b Inje University   (South Korea)

Author keywords

Bayesian networks; Feature abstraction; Gene drug dependency analysis; Microarray

Indexed keywords

5 AZA 2' DEOXYCYTIDINE; ANTINEOPLASTIC AGENT; ASPARTATE AMMONIA LIGASE; AZACITIDINE; BETA1 INTEGRIN; COLCHICINE; CYSTEINE DERIVATIVE; DIHYDROPYRIMIDINE DEHYDROGENASE; DNA; FLUOROURACIL; PACLITAXEL; PROUROKINASE; VINBLASTINE SULFATE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CANCER CELL; CANCER CELL CULTURE; CONTROLLED STUDY; CORRELATION ANALYSIS; DNA MICROARRAY; DRUG ACTIVITY; DRUG DNA INTERACTION; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMAN; HUMAN CELL; INFORMATION PROCESSING; LEARNING; LITERATURE; NONHUMAN; NUCLEOTIDE SEQUENCE; PROBABILITY; STATISTICAL DISTRIBUTION;

ALGORITHMS; ANTINEOPLASTIC AGENTS; ARTIFICIAL INTELLIGENCE; DATABASES, FACTUAL; DRUG DESIGN; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; NEOPLASM PROTEINS; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; SIGNAL TRANSDUCTION;

EID: 14544287454     PISSN: 02197200     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219720005000874     Document Type: Article

References (24)

1. Scherf U, Ross DT, Waltham M, Smith LH, Lee JK, Tanabe L, Kohn KW, Reinhold WC, Myers TG, Andrews DT, Scudiero DA, Eisen MB, Sausville EA, Pommier Y, Botstein D, Brown PO, Weinstein JN, A gene expression database for the molecular pharmacology of cancer, Nat Genet 24 3):236-244, 2000.
2. Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS, Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks, Proc Natl Acad Sci USA 97 22):12182-12186, 2000.
3. Friedman N, Linial M, Nachman I, Pe'er D, Using Bayesian networks to analyze expression data, J Comput Biol 7(3/4):601-620, 2000.
4. Pe'er D, Regev A, Elidan G, Friedman N, Inferring subnetworks from perturbed expression profiles, Bioinformatics 17(Suppl 1):S215-5224, 2001.
5. Hartemink AJ, Gifford DK, Jaakkola TS, Young RA, Combining location and expression data for principled discovery of genetic regulatory network models, Pacific Symposium on Biocomputing 7:437-449, 2002.
6. Imoto S, Kim S, Goto T, Aburatani S, Tashiro K, Kuhara S, Miyano S, Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network, J Bioinform Comput Biol 1 2):231-252, 2003.
7. Imoto S, Savoie CJ, Aburatani S, Kim S, Tashiro K, Kuhara S, Miyano S, Use of gene networks for identifying and validating drug targets, J Bioinform Comput Biol 1(3):459-474, 2003.
8. Toh H, Horimoto K, Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling, Bioinformatics 18(2):287-297, 2002.
9. Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, Friedman N, Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data, Nat Genet 34(2):166-176, 2003.
10. Friedman N, Goldszmidt M, Learning Bayesian networks with local structure, in Jordan MI (ed.), Learning in Graphical Models, pp. 421-459, MIT Press, Cambridge, 1999.
11. Heckerman D, Geiger D, Chickering DM, Learning Bayesian networks: The combination of knowledge and statistical data, Mach Learn 20 3):197-243, 1995.
12. Friedman N, Koller D, Being Bayesian about network structure: A Bayesian approach to structure discovery in Bayesian networks, Mach Learn 50(1):95-125, 2003.
13. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM, Systematic determination of genetic network architecture, Nat Genet 22 3):281-285, 1999.
14. Slonim N, Friedman N, Tishby N, Unsupervised document classification using sequential information maximization, in Proceedings of the 25th ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 129-136, 2002.
15. Dhillon IS, Modha DM, Concept decompositions for large sparse text data using clustering, Mach Learn 42(1): 143-175 2001.
16. Lin J, Divergence measures based on the Shannon entropy, IEEE T Inform Theor 37(1):145-151, 1991.
17. Herrero J, Valencia A, Dopazo J, A hierarchical unsupervised growing neural network for clustering gene expression patterns, Bioinformatics 17(2):126-136, 2001.
18. Lukashin AV, Fuchs R, Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters, Bioinformatics 17(5):405-414, 2001.
19. Chang J-H, Hwang K-B, Zhang B-T, Analysis of gene expression profiles and drug activity patterns by clustering and Bayesian network learning, in Lin SM, Johnson KE, (eds.), Methods of Microarray Data Analysis II (Proceedings of CAMDA'01), pp. 169-184, Kluwer Academic Publishers, 2002.
20. Cooper GF, Computational complexity of probabilistic inference using Bayesian belief networks, Artif Intell 42(2-3):393-405, 1990.
21. Chun MH, Plasmin induces the formation of multicellular spheroids of breast cancer cells, Cancer Lett 117(1):51-56, 1997.
22. Aoudjit F, Vuori K, Integrin signaling inhibits paclitaxel-induced apoptosis in breast cancer cells, Oncogene 20 36):4995-5004, 2001.
23. Magdinier F, Wolffe AP, Selective association of the methyl-CpG binding protein MBD2 with the silent p14/p16 locus in human neoplasia, Proc Natl Acad Sci USA 98(9):4990-4995, 2001.
24. Lindstroem MS, Klangby U, Wiman KG, p14ARF homozygous deletion or MDM2 overexpression in Burkitt lymphoma lines carrying wild type p53, Oncogene 20(17): 2171-2177, 2001.