Biological network inference using redundancy analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4414 LNBI, Issue , 2007, Pages 16-27

  Meyer, Patrick E ^a   Kontos, Kevin ^a   Bontempi, Gianluca ^a  

^a UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PROCESSING; FEATURE EXTRACTION; GENETIC ALGORITHMS; MICROARRAYS; REDUNDANCY;

GENETIC NETWORKS; MAXIMUM RELEVANCE - MINIMUM REDUNDANCY (MRMR); MRNET; RELEVANCE NETWORKS (RELNET);

INTERFACES (COMPUTER);

EID: 34548102489     PISSN: 03029743     EISSN: 16113349     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-71233-6_2     Document Type: Conference Paper

References (28)

1. Ideker, T., Galitski, T., Hood, L.: A new approach to decoding life: Systems biology. Annual Review of Genomics and Human Genetics 2 (2001) 343-372
2. van Someren, E.P., Wessels, L.F.A., Backer, E., Reinders, M.J.T.: Genetic network modeling. Pharmacogenomics 3 (2002) 507-525
3. Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. In Altman, R.B., Dunker, A.K., Hunter, L., Klein, T.E., eds.: Pacific Symposium on Biocomputing. Volume 3., Singapore, World Scientific Publishing (1998) 18-29
4. Akutsu, T., Miyano, S., Kuhara, S.: Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics 18 (2000) 727-734
5. Shmulevich, I., Dougherty, E., Kim, S., Zhang, W.: Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18 (2002) 261-274
6. Friedman, N., Linial, M., Nachman, I., Pe'er, D.: Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology 7 (2000) 601-620
7. Pe'er, D., Regev, A., Elidan, G., Friedman, N.: Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 (2001) S215-S224
8. van Berlo, R.J.P., van Someren, E.P., Reinders, M.J.T.: Studying the conditions for learning dynamic bayesian networks to discover genetic regulatory networks. SIMULATION 79 (2003) 689-702
9. Butte, A.J., Kohane, I.S.: Mutual information relevance networks: Functional genomic clustering using pairwise entropy measurments. Pacific Symposium on Biocomputing 5 (2000) 415-426
10. de la Fuente, A., Bing, N., Hoeschele, I., Mendes, P.: Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20 (2004) 3565-3574
11. Schäfer, J., Strimmer, K.: An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21 (2005) 754-764
12. Margolin, A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R., Califano, A.: ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7 (2006) S7
13. Tourassi, G., Frederick, E., Markey, M., Floyd Jr, C.: Application of the mutual information criterion for feature selection in computer-aided diagnosis. Medical Physics 28 (2001) 2394
14. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3 (2005) 185-205
15. Meyer, P.E., Bontempi, G.: On the use of variable complementarity for feature selection in cancer classification. Lecture Notes in Computer Science 3907 (2006) 91-102
16. Butte, A., Tamayo, P., Slonim, D., Golub, T., Kohane, I.: Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proceedings of the National Academy of Sciences 97 (2000) 12182-12186
17. Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley, New York (1990)
18. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005) 1226-1238
19. Merz, P., Freisleben, B.: Greedy and local search heuristics for unconstrained binary quadratic programming. Journal of Heuristics 8 (2002) 1381-1231
20. Rogers, S., Girolami, M.: A Bayesian regression approach to the inference of regulatory networks from gene expression data. Bioinformatics 21 (2005) 3131-3137
21. Van den Bulcke, T., Van Leemput, K., Naudts, B., van Remortel, P., Ma, H., Verschoren, A., De Moor, B., Marchai, K.: SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinformatics 7 (2006) 43
22. Paninski, L.: Estimation of entropy and mutual information. Neural Computation 15 (2003) 1191-1253
23. Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. Proceedings of the Twelfth International Conference on Machine Learning 202 (1995) 194-202
24. Beirlant, J., Dudewica, E., Gyofi, L., van der Meulen, E.: Nonparametric entropy estimation: An overview. Journal of Statistics (97)
25. Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing induction algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA (1998) 445-453
26. Bockhorst, J., Craven, M.: Markov networks for detecting overlapping elements in sequence data. In Saul, L.K., Weiss, Y., Bottou, L., eds.: Advances in Neural Information Processing Systems 17. MIT Press, Cambridge, MA (2005) 193-200
27. Chakrabarti, S.: Mining the Web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann (2003)
28. van Rijsbergen, C.J.: Information Retrieval. Buttersworth, London (1979)