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How species with similar repertoires of protein-coding genes differ so markedly at the phenotypic level is poorly understood. By comparing organ transcriptomes from vertebrate species spanning ~350 million years of evolution, we observed significant differences in alternative splicing complexity between vertebrate lineages, with the highest complexity in(More)
The increasing availability of network data is creating a great potential for knowledge discovery from graph data. In many applications, feature vectors are given in addition to graph data, where nodes represent entities, edges relationships between entities, and feature vectors associated with the nodes represent properties of entities. Often features and(More)
MOTIVATION Recent genomic studies have confirmed that cancer is of utmost phenotypical complexity, varying greatly in terms of subtypes and evolutionary stages. When classifying cancer tissue samples, subnetwork marker approaches have proven to be superior over single gene marker approaches, most importantly in cross-platform evaluation schemes. However,(More)
BACKGROUND Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when(More)
Understanding evolutionary dynamics from a systemic point of view crucially depends on knowledge about how evolution affects size and structure of the organisms' functional building blocks (modules). It has been recently reported that statistics over sparse PPI graphlets can robustly monitor such evolutionary changes. However, there is abundant evidence(More)
Intrinsically disordered regions have been associated with various cellular processes and are implicated in several human diseases, but their exact roles remain unclear. We previously defined two classes of conserved disordered regions in budding yeast, referred to as "flexible" and "constrained" conserved disorder. In flexible disorder, the property of(More)
Large-scale gene expression experiments and interaction networks have become major data sources for discovery in systems biology. In several types of interaction networks, as is widely established, active modules, i.e. functional, simultaneously active groups of genes, are best encoded as highly interconnected regions that are co-expressed and show(More)
UNLABELLED ELASPIC is a novel ensemble machine-learning approach that predicts the effects of mutations on protein folding and protein-protein interactions. Here, we present the ELASPIC webserver, which makes the ELASPIC pipeline available through a fast and intuitive interface. The webserver can be used to evaluate the effect of mutations on any protein in(More)
MOTIVATION Rapid advances in genotyping and genome-wide association studies have enabled the discovery of many new genotype-phenotype associations at the resolution of individual markers. However, these associations explain only a small proportion of theoretically estimated heritability of most diseases. In this work, we propose an integrative mixture model(More)
Flexible module discovery by exhaustive search for densely connected, co-expressed interaction sub-networks: Figure motivating our choice of density threshold and description of the algorithms of the benchmarking competitors. Here, we describe the algorithmic ideas of the comparison partners of subsection 3.1 of the Results section of the main paper in more(More)