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Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein- protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein- protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction. [ABSTRACT FROM AUTHOR]

Clustering is a popular technique for analyzing microarray data sets, with n genes and m experimental conditions. As explored by biologists, there is a real need to identify coregulated gene clusters, which include both positive and negative regulated gene clusters. The existing pattern-based and tendency-based clustering approaches cannot directly be applied to find such coregulated gene clusters, because they are designed for finding positive regulated gene clusters. In this paper, in order to cluster coregulated genes, we propose a coding scheme that allows us to cluster two genes into the same cluster if they have the same code, where two genes that have the same code can be either positive or negative regulated. Based on the coding scheme, we propose a new algorithm for finding maximal subspace coregulated gene clusters with new pruning techniques. A maximal subspace coregulated gene cluster clusters a set of genes on a condition sequence such that the cluster is not included in any other subspace coregulated gene clusters. We conduct extensive experimental studies. Our approach can effectively and efficiently find maximal subspace coregulated gene clusters. In addition, our approach outperforms the existing approaches for finding positive regulated gene clusters. [ABSTRACT FROM AUTHOR]

Oxidative stress has been indicated in a variety of pathological processes such as atherosclerosis, diabetes, and neurodegenerative diseases. Understanding how intracellular signaling pathways respond to oxidative insults such as hydrogen peroxide (H2O2) would have significant therapeutic implications. Recent genetic studies have placed apoptosis signal-regulating kinase 1 (ASK1) in a pivotal position in transmitting H2O2-initiated signals. How ASK1 is activated by H2O2, though, remains a subject of intense investigation. Here we report a mechanism by which H2O2 induces ASK1 activation through dynamic control of its phosphorylation at serine 967. We found that treatment of COS7 cells with H2O2 triggers dephosphorylation of Ser-967 through an okadaic acid-sensitive phosphatase, resulting in dissociation of the ASK1·14-3-3 complex with concomitant increase of ASK1 catalytic activity and ASKl-mediated activation of JNK and p38 pathways. [ABSTRACT FROM AUTHOR]