Biomedical subjects

Yu-dong Cai

Publications and source records attributed to Yu-dong Cai.

4 recordsLinked to original sources

PubMed2006

Operon prediction based on SVM.

The operon is a specific functional organization of genes found in bacterial genomes. Most genes within operons share common features. The support vector machine (SVM) approach is here used to predict operons at the genomic level. Four features were chosen as SVM input vectors: the intergenic distances, the number of common pathways, the number of conserved gene pairs and the mutual information of phylogenetic profiles. The analysis reveals that these common properties are indeed characteristic of the genes within operons and are different from that of non-operonic genes. Jackknife testing indicates that these input feature vectors, employed with RBF kernel SVM, achieve high accuracy. To validate the method, Escherichia coli K12 and Bacillus subtilis were taken as benchmark genomes of known operon structure, and the prediction results in both show that the SVM can detect operon genes in target genomes efficiently and offers a satisfactory balance between sensitivity and specificity.

Bacillus subtilis↗

PubMed2004

Identify catalytic triads of serine hydrolases by support vector machines.

The core of an enzyme molecule is its active site from the viewpoints of both academic research and industrial application. To reveal the structural and functional mechanism of an enzyme, one needs to know its active site; to conduct structure-based drug design by regulating the function of an enzyme, one needs to know the active site and its microenvironment as well. Given the atomic coordinates of an enzyme molecule, how can we predict its active site? To tackle such a problem, a distance group approach was proposed and the support vector machine algorithm applied to predict the catalytic triad of serine hydrolase family. The success rate by jackknife test for the 139 serine hydrolases was 85%, implying that the method is quite promising and may become a useful tool in structural bioinformatics.

Algorithms↗

PubMed2004

A novel approach to predict active sites of enzyme molecules.

Enzymes are critical in many cellular signaling cascades. With many enzyme structures being solved, there is an increasing need to develop an automated method for identifying their active sites. However, given the atomic coordinates of an enzyme molecule, how can we predict its active site? This is a vitally important problem because the core of an enzyme molecule is its active site from the viewpoints of both pure scientific research and industrial application. In this article, a topological entity was introduced to characterize the enzymatic active site. Based on such a concept, the covariant discriminant algorithm was formulated for identifying the active site. As a paradigm, the serine hydrolase family was demonstrated. The overall success rate by jackknife test for a data set of 88 enzyme molecules was 99.92%, and that for a data set of 50 independent enzyme molecules was 99.91%. Meanwhile, it was shown through an example that the prediction algorithm can also be used to find any typographic error of a PDB file in annotating the constituent amino acids of catalytic triad and to suggest a possible correction. The very high success rates are due to the introduction of a covariance matrix in the prediction algorithm that makes allowance for taking into account the coupling effects among the key constituent atoms of active site. It is anticipated that the novel approach is quite promising and may become a useful high throughput tool in enzymology, proteomics, and structural bioinformatics.

Algorithms↗

PubMed2003

Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence.

Classification of gene function remains one of the most important and demanding tasks in the post-genome era. Most of the current predictive computer methods rely on comparing features that are essentially linear to the protein sequence. However, features of a protein nonlinear to the sequence may also be predictive to its function. Machine learning methods, for instance the Support Vector Machines (SVMs), are particularly suitable for exploiting such features. In this work we introduce SVM and the pseudo-amino acid composition, a collection of nonlinear features extractable from protein sequence, to the field of protein function prediction. We have developed prototype SVMs for binary classification of rRNA-, RNA-, and DNA-binding proteins. Using a protein's amino acid composition and limited range correlation of hydrophobicity and solvent accessible surface area as input, each of the SVMs predicts whether the protein belongs to one of the three classes. In self-consistency and cross-validation tests, which measures the success of learning and prediction, respectively, the rRNA-binding SVM has consistently achieved >95% accuracy. The RNA- and DNA-binding SVMs demonstrate more diverse accuracy, ranging from approximately 76% to approximately 97%. Analysis of the test results suggests the directions of improving the SVMs.

Computational Biology↗