Mining Yeast Transcriptional Regulatory Modules from Factor DNA-Binding Sites and Gene Expression Data

In eukaryotes, gene expression is controlled by various transcription factors that bind to the promoter regions. Transcription factors may act positively, negatively or not at all. Di#erent combinations of them may also activate or repress gene expression, and form regulatory networks of transcription. Uncovering such regulatory networks is a central challenge in genomic biology

Prediction and Analysis of β-Turns in Proteins by Support Vector Machine

Tight turn has long been recognized as one of the three important features of proteins after the #-helix and #-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are #-turns. Analysis and prediction of #-turns in particular and tight turns in general are very useful for the design of new molecules such as drugs, pesticides, and antigens. In this paper, we introduce a support vector machine (SVM) approach to prediction and analysis of #-turns. We have investigated two aspects of applying SVM to the prediction and analysis of #-turns. First, we developed a new SVM method, called BTSVM, which predicts #-turns of a protein from its sequence. The prediction results on the dataset of 426 non-homologous protein chains by sevenfold cross-validation technique showed that our method is superior to the other previous methods. Second, we analyzed how amino acid positions support (or prevent) the formation of #-turns based on the "multivariable" classification model of a linear SVM. This model is more general than the other ones of previous statistical methods. Our analysis results are more comprehensive and easier to use than previously published analysis results

microRNA expression profiles for classification and analysis of tumor samples

The paper presents a computational method to (1) predicting the tumor tissues using high-throughput miRNA expression profiles; (2) finding the informative miRNAs that show strong distinction of expression level in tumor tissues

Prediction of Histone Modifications in DNA sequences

DNA molecules are wrapped around histone octamers to form nucleosome structures whose occupancy and histone modification states profoundly influence the gene expression. Depending on the DNA segment that a nuleosome incorporated, its histone proteins exihibit paticular modifications by added some functional chemical groups to specific amino acids. The key approach up to now to determining the DNA locations of histone occupancy as well as histone modifications is an experimental technique called ChiP-Chip, or Chromatin Immunoprecipitation on Microarray Chip. This experimental technique has some disadvantages such as it is tedious, wastes time and money, produces noise, and cannot provide results at an arbitrarily high resolution, especially with large genomes like human's. We have developed a computational method to determine qualitatively histone-occupied as well as acetylation and methylation locations in DNA sequences. The method is based on support vector machines (SVMs) to learn models from training data sets that discriminate between areas with high and low levels of histone occupancy, acetylation or methylation. Our computational method can give quickly the prediction at any position in a DNA sequence based on the content and context of the subsequence around that position. The prediction results on the yeast genome by three-fold cross-validation showed high accuracy and were consistent with the ones from experimental methods. Moreover, SVM-classification models in our method can present genetic preferences of DNA areas that have high modification levels