Discriminating transmembrane proteins from signal peptides using SVM-Fisher approach

Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications

Volumn 2005, Issue , 2005, Pages 151-155

  Kahsay, Robel Y ^a   Gao, Guang R ^a   Liao, Li ^b  

^a UNIVERSITY OF DELAWARE   (United States)

^b University of Delaware   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACIDS; COMPUTATIONAL METHODS; LEARNING SYSTEMS; MARKOV PROCESSES; MATHEMATICAL MODELS;

HIDDEN MARKOV MODELS; HYDROPHOBIC DOMAINS; SIGNAL PEPTIDES; TOPOLOGY PREDICTION; TRANSMEMBRANE PROTEINS;

PROTEINS;

EID: 33847302046     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2005.24     Document Type: Conference Paper

References (8)

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2. L. Kail, A. Krogh, E. Sonnhammer "A combined transmembrane topology and signal peptide prediction method," Journal of Molecular Biology, 338, pp. 1027-1036.
3. A. Krogh, B. Larsson, G. von Heijne, and E. Sonnhammer, "Prediction Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes," Journal of Molecular Biology, 305, pp. 567-580.
4. T. Jaakkola, M. Diekhans, D. Haussier "A discriminative framework for detecting remote protein homologies" Computational Biology, 7, pp. 95-114.
5. E. Sonnhammer, G. von Heijne, and A. Krogh, "A hidden Markov model for predicting transmembrane helices in protein sequences," Proceedings of ISMB 6, 1998, pp 175-182.
6. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison, Biological sequence analysis: probabilistic models of proteins and nucleic acids, Cambridge University Press, 1998.
7. T. Joachims Making large-Scale SVM Learning Practical, B. Schlkopf and C. Burges and A. Smola (ed.), MIT-Press, Cambridge
8. A. Krogh "Hidden Markov models for labeled sequences," In Proc. of the 12th IAPR International Conference on Pattern Recognition, IEEE Computer Society Press, Los Alamitos, CA, pp. 140-44.