A feature-based approach to modeling protein-DNA interactions

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

Volumn 4453 LNBI, Issue , 2007, Pages 77-91

  Sharon, Eilon ^a   Segal, Eran ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

Author keywords

DNA sequence motifs; Markov networks; Motif finder; Probabilistic graphical models; Transcription factor binding sites

Indexed keywords

ALGORITHMS; BINDING SITES; DNA; MARKOV PROCESSES; MATHEMATICAL MODELS; YEAST;

DNA SEQUENCE MOTIFS; MARKOV NETWORKS; MOTIF FINDERS; PROBABILISTIC GRAPHICAL MODELS; TRANSCRIPTION FACTOR BINDING SITES;

TRANSCRIPTION FACTORS;

EID: 34547395847     PISSN: 03029743     EISSN: 16113349     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-71681-5_6     Document Type: Conference Paper

References (16)

1. Elnitski, L. et al.: Locating mammalian transcription factor binding sites: A survey of computational and experimental techniques. Genome Res 16(12) (2006) 1455-64
2. Bulyk, M.L.: Dna microarray technologies for measuring protein-dna interactions. Current Opinion in Biotechnology 17 (2006) 1-9
3. Maerkl, S.J., Quake, S.R.: A systems approach to measuring the binding energy landscapes of transcription factors. Science 315(5809) (2007) 233-236
4. Barash, Y., Elidan, G., Friedman, N., Kaplan, T.: Modeling dependencies in protein-dna binding sites. In: RECOMB. (2003)
5. Harbison, CT. et al.: Transcriptional regulatory code of a eukaryotic genome. Nature 431(7004) (2004) 99-104
6. MacIsaac, K. et al.: An improved map of conserved regulatory sites for saccharomyces cerevisiae. BMC Bioinformatics 7 (2006) 113
7. Della Pietra, S. et al.: Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(4) (1997) 380-393
8. Lee, S.I., Ganapathi, V., Koller, D.: Efficient structure learning of Markov networks using L1-regularization. In: NIPS. (2007)
9. Perkins, S., Lacker, K., Theiler, J.: Grafting: fast, incremental feature selection by gradient descent in function space. J. Mach. Learn. Res. 3 (2003) 1333-1356
10. Minka, T.P.: Algorithms for maximum-likelihood logistic regression. Technical Report 758, Carnegie Mellon University (2001)
11. Yedidia, J.S. et al.: Generalized belief propagation. In: NIPS. (2000) 689-695
12. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal. Statist. Soc B. 58(1) (1996) 267-288
13. Ng, A.: Feature selection, 11 vs. 12 regularization, and rotational invariance. In: ICML.(2004)
14. Rothermel, B., Thornton, J., Butow, R.: Rtgp3, a basic helix-loop-helix/leucine zipper protein that functions in mitochondrial-induced changes in gene expression, contains independent activation domains. J Biol Chem. 272 (1997) 19801-7
15. Zeitlinger, J. et al.: Program-specific distribution of a transcription factor dependent on partner transcription factor and mapk signaling. Cell 113(3) (2003) 395-404
16. Segal, E. et al.: Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics 19(Suppl 1) (2003) S273-82