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BMCBI
2008
147views more  BMCBI 2008»
13 years 5 months ago
Transmembrane helix prediction using amino acid property features and latent semantic analysis
Background: Prediction of transmembrane (TM) helices by statistical methods suffers from lack of sufficient training data. Current best methods use hundreds or even thousands of f...
Madhavi Ganapathiraju, Narayanas Balakrishnan, Raj...
BMCBI
2006
137views more  BMCBI 2006»
13 years 5 months ago
A maximum likelihood framework for protein design
Background: The aim of protein design is to predict amino-acid sequences compatible with a given target structure. Traditionally envisioned as a purely thermodynamic question, thi...
Claudia L. Kleinman, Nicolas Rodrigue, Céci...
BMCBI
2010
159views more  BMCBI 2010»
13 years 5 months ago
Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines
Background: Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods ha...
Alvaro J. González, Li Liao
BMCBI
2004
123views more  BMCBI 2004»
13 years 5 months ago
Interaction profile-based protein classification of death domain
Background: The increasing number of protein sequences and 3D structure obtained from genomic initiatives is leading many of us to focus on proteomics, and to dedicate our experim...
Drew Lett, Michael Hsing, Frederic Pio
ISMB
1994
13 years 7 months ago
Stochastic Motif Extraction Using Hidden Markov Model
In this paper, westudy the application of an ttMM(hidden Markov model) to the problem of representing protein sequencesby a stochastic motif. Astochastic protein motif represents ...
Yukiko Fujiwara, Minoru Asogawa, Akihiko Konagaya