BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Applications of evolutionary SVM to prediction of membrane alpha-helices
Expert Systems with Applications: An International Journal
Signal peptide discrimination and cleavage site identification using SVM and NN
Computers in Biology and Medicine
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Most computational methods for transmembrane protein topology prediction rely on compositional bias of amino acids to locate those hydrophobic domains in transmembrane proteins. Because signal peptides also contain hydrophobic segments, these computational prediction methods often misidentify signal peptides as transmembrane proteins. Here, we present a new approach that combines the SVM-Fisher discrimination method and TMMOD - a hidden Markov model based predictor for transmembrane proteins. While TMMOD alone has already outperformed most existing methods in both identification and topology prediction, this new approach further improves the ability of TMMOD to discriminate between transmembrane proteins and signal peptide containing proteins, reducing mis-prediction of signal peptides by more than 30% in our test data.