In this work we address the problem of predicting proteinprotein interactions. Its solution can give greater insight in the study of complex diseases, like cancer, and provides valuable information in the study of active small molecules for new drugs, limiting the number of molecules to be tested in laboratory. We model the problem as a binary classification task, using a suitable coding of the...

Loss of the lysosomal ClC-7/Ostm1 2Cl /H exchanger causes lysosomal storage disease and osteopetrosis in humans and additionally changes fur colour in mice. Its conversion into a Cl conductance in Clcn7 mice entails similarly severe lysosomal storage, but less severe osteopetrosis and no change in fur colour. To elucidate the basis for these phenotypical differences, we generated Clcn7 mice exp...

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological text mining. The BioCreative track 4 provides training data and an evaluation environment for the extraction of causal relationships in Biological Expression Language (BEL). BEL is a modeling language that is easily editable by humans or by automatic systems and can express cau...

This review examines protein complexes in the Brookhaven Protein Databank to gain a better understanding of the principles governing the interactions involved in protein-protein recognition. The factors that influence the formation ofprotein-protein complexes are explored in four different types of proteinprotein complexes-homodimeric proteins, heterodimeric proteins, enzymeinhibitor complexes,...

The exponential increase in publication rate of new articles is limiting access of researchers to relevant literature. This has prompted the use of text mining tools to extract key biological information. Previous studies have reported extensive modification of existing generic text processors to process biological text. However, this requirement for modification had not been examined. In this ...

Abstract: The recent availability of large data sets of proteinprotein-interactions (PPIs) from various species offers new opportunities for functional genomics and proteomics. We describe a method for exploiting conserved and connected subgraphs (CCSs) in the PPI networks of multiple species for the prediction of protein function. Structural conservation is combined with functional conservatio...

As the amount of on-line scientific literature in the biomedical domain increases, automatic processing has become a promising approach for accelerating research. We are applying syntactic parsing trained on the general domain to identify proteinprotein interactions. One of the main difficulties obstructing the use of language processing is the prevalence of specialized terminology. Accordingly...

A promising way to interfere with biological processes is through the control of protein-protein interactions by means of small molecules that modulate the formation of protein-protein complexes. Although the feasibility of this approach has been demonstrated in principle by recent results, many of the small-molecule modulators known to date have not been found by rational design approaches. In...

The protein-protein interactions (PPIs) are crucial for understanding the majority of cellular processes. PPIs play important role in gene transcription regulation, cellular signaling, molecular basis of immune response and more. Moreover, a disruption of these mechanisms is frequently postulated as a possible cause of diseases such as Alzheimers or cancer. For many of biologically relevant cas...