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Background: Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins. Results: In this paper, we propose a new algorithm that uses Regularized Least Squares Classifier (RLSC) in conjunction with multiple physicochemical properties of amino acids to predict odorantbinding proteins. The algorithm was applied to the dataset derived from Pfam and GenDiS database and we obtained overall prediction accuracy of 97.7% (94.5% and 98.4% for positive and negative classes respectively). Conclusion: Our study suggests that RLSC is potentially useful for predicting the odorant binding proteins from sequence-derived properties irrespective of sequence similarity. Our method predicts 92.8% of 56 odorant binding proteins non-homologous to any protein in the swissprot database and 97.1% of the 414 independent dataset proteins, suggesting the usefulness of RLSC method for facilitating the prediction of odorant binding proteins from sequence information. [ABSTRACT FROM AUTHOR]

Background: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary to conduct the discriminant analysis with all the genes. Hence, gene selection is usually performed to select important genes., Results: A gene selection method searches for an optimal or near optimal subset of genes with respect to a given evaluation criterion. In this paper, we propose a new evaluation criterion, named the leave-one-out calculation (LOOC, A list of abbreviations appears just above the list of references) measure. A gene selection method, named leave-one-out calculation sequential forward selection (LOOCSFS) algorithm, is then presented by combining the LOOC measure with the sequential forward selection scheme. Further, a novel gene selection algorithm, the gradient-based leave-one-out gene selection (GLGS) algorithm, is also proposed. Both of the gene selection algorithms originate from an efficient and exact calculation of the leave-one-out cross-validation error of the least squares support vector machine (LS-SVM). The proposed approaches are applied to two microarray datasets and compared to other well-known gene selection methods using codes available from the second author., Conclusion: The proposed gene selection approaches can provide gene subsets leading to more accurate classification results, while their computational complexity is comparable to the existing methods. The GLGS algorithm can also better scale to datasets with a very large number of genes.