Inductive logic programming for gene regulation prediction

Machine Learning

Volumn 70, Issue 2-3, 2008, Pages 225-240

  Fröhler, Sebastian ^a   Kramer, Stefan ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Gene expression; Gene regulation; Inductive logic programming; Relational learning; Systems biology

Indexed keywords

BINDING SITES; DECODING; LEARNING SYSTEMS; LOGIC PROGRAMMING; MATHEMATICAL MODELS; PROTEINS;

GENE REGULATION; INDUCTIVE LOGIC PROGRAMMING; RELATIONAL LEARNING; SYSTEMS BIOLOGY;

GENE EXPRESSION;

EID: 37849043459     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-007-5037-3     Document Type: Conference Paper

References (20)

1. Allocco, D. J., Kohane, I. S., & Butte, A. J. (2004). Quantifying the relationship between co-expression and co-regulation and gene function. BMC Bioinformatics, 5(18)
2. Blockeel, H., & Raedt, L. D. (1998). Top-down induction of first-order logical decision trees. Artificial Intelligence, 101(1-2), 285-297.
3. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
4. Dez, C., & Tollervey, D. (2004). Ribosome synthesis meets the cell cycle. Current Opinion in Microbiology, 7(6), 631-637.
5. Freund, Y., & Mason, L. (1999). The alternating decision tree learning algorithm. In Proceedings 16th international conference on machine learning (ICML 1999) (pp. 124-133). Los Altos: Kaufmann.
6. Fröhler, S. (2006). Machine learning for gene regulation prediction. Diploma thesis, TU München.
7. Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., & Brown, P. O. (2000). Genomic expression programs in the response of yeast cells to environmental changes. Molecular Biology of the Cell, 11(12), 4241-4257.
8. Jeong, J., Johns, J., Sinclair, C., Park, J., & Rossie, S. (2003). Characterization of Saccharomyces cerevisiae protein Ser/Thr phosphatase T1 and comparison to its mammalian homolog PP5. BMC Cell Biology, 4(3).
9. Latchman, D. (2005). Gene regulation: a eukaryotic perspective (5th ed.). London: Taylor & Francis.
10. Mewes, H., Albermann, K., Heumann, K., Liebl, S., & Pfeiffer, F. (1997). MIPS: a database for protein sequences, homology data and yeast genome information. Nucleic Acids Research, 25(1), 28-30.
11. Middendorf, M., Kundaje, A., Wiggins, C., Freund, Y., & Leslie, C. (2004). Predicting genetic regulatory response using classification. Bioinformatics, 20(suppl_1), 232-240.
12. Ong, I., Page, D., & Santos Costa, V. (2006). Inferring regulatory networks from time series expression data and relational data via inductive logic programming. In Proceedings 16th International Conference on Inductive Logic Programming (ILP 2006), short papers.
13. Park, H.-O., & Craig, E. A. (1989). Positive and negative regulation of basal expression of a Yeast HSP70 gene. Molecular and Cellular Biology, 9(5), 2025-2033.
14. Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Guldener, U., Mannhaupt, G., Munsterkotter, M., & Mewes, H. W. (2004). The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Research, 32(18), 5539-5545.
15. Segal, E., Shapira, M., Regev, A., Pe'er, D., Botstein, D., Koller, D., & Friedman, N. (2003). Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Reviews Genetics, 34(2), 166-167.
16. van Helden, J., Andr, B., & Collado-Vides, J. (1998). Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. Journal of Molecular Biology, 281, 827-842.
17. Wiederrecht, G., Seto, D., & Parker, C. (1988). Isolation of the gene encoding the S. cerevisiae heat shock transcription factor. Cell, 54(6), 841-853.
18. Wingender, E., Dietze, P., Karas, H., & Knueppel, R. (1996). TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Research, 24(1), 238-241.
19. Witten, I. H., & Frank, E. (2005). Data mining: practical machine learning tools and techniques (2nd ed.). Los Altos: Kaufmann.
20. www.yeastgenome.org