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MOTIVATION Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. RESULTS In this paper, Support Vector Machine has been introduced to predict the subcellular localization of(More)
Systems biology efforts are increasingly adopting quantitative, mechanistic modeling to study cellular signal transduction pathways and other networks. However, it is uncertain whether the particular set of kinetic parameter values of the model closely approximates the corresponding biological system. We propose that the parameters be assigned statistical(More)
In this work we have developed a new framework for microarray gene expression data analysis. This framework is based on hidden Markov models. We have benchmarked the performance of this probability model-based clustering algorithm on several gene expression datasets for which external evaluation criteria were available. The results showed that this approach(More)
A high-performance method was developed for protein secondary structure prediction based on the dual-layer support vector machine (SVM) and position-specific scoring matrices (PSSMs). SVM is a new machine learning technology that has been successfully applied in solving problems in the field of bioinformatics. The SVM's performance is usually better than(More)
BACKGROUND Protein palmitoylation, an essential and reversible post-translational modification (PTM), has been implicated in cellular dynamics and plasticity. Although numerous experimental studies have been performed to explore the molecular mechanisms underlying palmitoylation processes, the intrinsic feature of substrate specificity has remained elusive.(More)
Protein methylation is an important and reversible post-translational modification of proteins (PTMs), which governs cellular dynamics and plasticity. Experimental identification of the methylation site is labor-intensive and often limited by the availability of reagents, such as methyl-specific antibodies and optimization of enzymatic reaction.(More)
Subcellular localization performs an important role in genome analysis as a key functional characteristic of proteins. Therefore, an automatic, reliable and efficient prediction system for protein subcellular localization is needed for large-scale genome analysis. This paper describes a new residue-couple model using a support vector machine to predict the(More)
Computational identification of transcription factor binding sites is an important research area of computational biology. Positional weight matrix (PWM) is a model to describe the sequence pattern of binding sites. Usually, transcription factor binding sites prediction methods based on PWMs require user-defined thresholds. The arbitrary threshold and also(More)
Identifying potential protein interactions is of great importance in understanding the topologies of cellular networks, which is much needed and valued in current systematic biological studies. The development of our computational methods to predict protein-protein interactions have been spurred on by the massive sequencing efforts of the genomic(More)