PRESS: PRotEin S-Sulfenylation server.

MOTIVATION: Transient S-sulfenylation of cysteine thiols mediated by reactive oxygen species plays a critical role in pathology, physiology and cell signaling. Therefore, discovery of new S-sulfenylated sites in proteins is of great importance towards understanding how protein function is regulated upon redox conditions. RESULTS: We developed PRESS (PRotEin S-Sulfenylation) web server, a server which can effectively predict the cysteine thiols of a protein that could undergo S-sulfenylation under redox conditions. We envisage that this server will boost and facilitate the discovery of new and currently unknown functions of proteins triggered upon redox conditions, signal regulation and transduction, thus uncovering the role of S-sulfenylation in human health and disease. AVAILABILITY AND IMPLEMENTATION: The PRESS web server is freely available at http://press-sulfenylation.cse.uoi.gr/ CONTACTS: agtzakos@gmail.com or gtzortzi@cs.uoi.gr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multiple view clustering using a weighted combination of exemplar-based mixture models.

Multiview clustering partitions a dataset into groups by simultaneously considering multiple representations (views) for the same instances. Hence, the information available in all views is exploited and this may substantially improve the clustering result obtained by using a single representation. Usually, in multiview algorithms all views are considered equally important, something that may lead to bad cluster assignments if a view is of poor quality. To deal with this problem, we propose a method that is built upon exemplar-based mixture models, called convex mixture models (CMMs). More specifically, we present a multiview clustering algorithm, based on training a weighted multiview CMM, that associates a weight with each view and learns these weights automatically. Our approach is computationally efficient and easy to implement, involving simple iterative computations. Experiments with several datasets confirm the advantages of assigning weights to the views and the superiority of our framework over single-view and unweighted multiview CMMs, as well as over another multiview algorithm which is based on kernel canonical correlation analysis.

The global kernel k-means algorithm for clustering in feature space.

Kernel k-means is an extension of the standard k -means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters, and, due to its incremental nature and search procedure, locates near-optimal solutions avoiding poor local minima. Furthermore, two modifications are developed to reduce the computational cost that do not significantly affect the solution quality. The proposed methods are extended to handle weighted data points, which enables their application to graph partitioning. We experiment with several data sets and the proposed approach compares favorably to kernel k -means with random restarts.