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Motivation We propose a novel method for automatic module extraction from protein-protein interaction networks. While most previous approaches for module discovery are based on graph partitioning, our algorithm can efficiently enumerate all densely connected modules in the network. As currently available interaction data are incomplete, this is a meaningful generalization of clique search techniques. In comparison with partitioning methods, the approach has the following advantages: the user can specify a minimum density for the outcoming modules and has the guarantee that all modules that satisfy this criterion are discovered. Moreover, it provides a natural way to detect overlapping modules. Many proteins are not steadily present in the cell, but are specifically expressed in dependence of cell type, environmental conditions, and developmental state. Therefore we introduce an additional constraint for modules which accounts for differential expression. Results We analysed human interaction data from MINT, Intact, HPRD, and DIP in the context of tissue-specific gene expression data in human provided by Su et al. We discretized the expression information into binary states (expressed versus not expressed) and searched for densely connected modules where all proteins are expressed in at least 3 tissues and all proteins are not expressed in at least 10 tissues. To deal with the fact that protein interaction data contain a high number of false positives, we computed reliability scores for each experimental source. Similarly to the work by Jansen et al., we used for that purpose a gold standard set of known interactions as well as a gold standard set of false interactions and calculated the likelihood ratio, which was used to assign edge weights to the interaction graph. The density of a module is defined as the sum of the edge weights inside the module divided by the maximal possible weight sum for a module of that size. Setting the minimum density threshold to 35% and removing modules that are totally contained in other modules, we obtained a set of 949 differentially expressed modules. They were ranked in descending order according to the average weight per node (see [5]), so larger and denser modules appear first. On the one hand, we discovered known complexes and modules that link strongly cooperating complexes like MCM and ORC. On the other hand, we found extensions of known complexes that confirm hypothetical functional annotation in Uniprot as well as modules which are not contained in the manually curated set of known complexes, but share the same functional annotation. Finally, some modules are candidates for further biological investigation, containing proteins with unknown functional relationships. Conclusion We developed a general method for exhaustive dense module extraction from networks. Remarkably, it allows to determine exact P-values for the predicted modules without having to rely on any network model and can easily integrate information from different heterogeneous data sources. [ABSTRACT FROM AUTHOR]

An abstract of the article related to event extraction to be used in the field of biomedical, with the help of linguistic corpora by Filip Ginter, Jari Björne and Sampo Pyysalo is presented.

Poster presentation Advances in high-throughput technologies such as ChIPchip and the completion of human genomic sequences allow analysis of the mechanisms of gene regulation on a systems level. In this study, we have developed a computational genomics approach (ChIPModules) and a motif discovery approach (ChIPMotifs) to mine the ChIP-chip data. The ChIPModules approach begins with experimentally determined binding sites and integrates positional weight matrices, a comparative genomics approach, and statistical learning methods to identify transcriptional regulatory modules. Using E2F1 ChIP-chip data performed on ENCODE regions in both HeLa and MCF7 cells, we have identified five regulatory modules for E2F1. One of modules was validated by using ChIP-chip with arrays containing ~14,000 human promoters. The ChIPMotifs approach incorporates a bootstrap re-sampling method to statistically infer the optimal cutoff threshold for a position weight matrix (PWM) of a motif identified from ChIP-chip data by ab initio motif discovery programs. Using OCT4 ChIP-chip data, we developed an in vivo OCT4 PWM. We then used this PWM and our ChIPModules to identify transcription factors co-localizing with OCT4 in a testicular germ cell tumor (Ntera2 cells). [ABSTRACT FROM AUTHOR]

Introduction Quantitative trait loci (QTL) are often positioned within large genomic regions that contain dozens to hundreds of genes. It proves difficult to identify underlying genes by direct experiments. Nevertheless, identification of candidate genes by database mining can be productive by defining the size of the subregion around the optimum QTL map position, coupled with q systematic key word search of genetic and published information. Using a recently developed software package, PGMapper [1], a systematic study has been conducted to examine candidate genes of QTL of type 1 diabetes according to all available literature reports in PubMed and Online Mendelian Inheritance in Man (OMIM). Materials and methods Literature search was conducted with key words "diabetes" and "QTL" in PubMed for every publication up to December 2007. Candidate gene search was conducted with PGMapper [1]. Genes within a QTL region were obtained from the Ensembl database http:// www.ensembl.org/index.html. For every known gene in a QTL, their potential connection with type 1 diabetes was evaluated by searching information from OMIM http:// www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM and PubMed http://www.ncbi.nlm.nih.gov/entrez/ query.fcgi?db=PubMed. Results and conclusions Our search produced some unexpected results. We examined 9038 genes within the interval of 26 QTL of type 1 diabetes, of which 138 genes were considered to be candidate genes for those QTL. On one hand, there are a large number of candidate genes located in each of those QTL; on the other hand, we also found that some obvious candidate genes of QTL have not yet been investigated. Thus, the comprehensive search of candidate genes for known QTL may yield unexpected benefit for identification of responsive genes underlying diabetes QTL. Our search also indicated that genome resources such as Ensembl database and bioinformatics tools such as PGMapper could be great assistance in searching for candidate genes of QTL. [ABSTRACT FROM AUTHOR]

A conference paper on nonlinear data mining of microarray data using Michaelis Menten functions is presented. It describes the use of nonlinear least squares to fit Michaelis-Menten functions to gene expression profiles. It also demonstrate the technique applied to an extensive series of experiments aimed at understanding anaerobic respiration in wild-type and FNR-mutant Escherichia coli.