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New Book
Medical Biostatistics for Complex Diseases
New book ‘Medical Biostatistics for Complex Diseases’
by Frank Emmert-Streib and Matthias Dehmer

This book provides a collection of highly valuable statistical and computational approaches designed for developing powerful methods to analyze large-scale high-throughput data derived from studies of complex diseases. Such diseases include cancer and cardiovascular disease, and constitute the major health challenges in industrialized countries. They are characterized by the systems properties of gene networks and their interrelations, instead of individual genes, whose malfunctioning manifests in pathological phenotypes, thus making the analysis of the resulting large data sets particularly challenging.

More information about this book can be found here.

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Tutorial at ISMB 2010 in Boston
As part of the ISMB 2010 (Intelligent Systems for Molecular Biology) taking place in Boston (USA) Frank Emmert-Streib and his colleague Galina Glazko from Rochester University will organize a tutorial with the title 'Pathway analysis of expression data: Deciphering functional building blocks of complex diseases'. ISMB is the major meeting of the International Society for Computational Biology (ISCB) and represents the largest bioinformatics/computational biology conference.

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EPRSC grant: Detecting pathological pathways of complex diseases
The EPSRC (Engineering and Physical Sciences Research Council) awarded a grant to Frank Emmert-Streib. The purpose of this grant is the development of a multivariate statistical method for the analysis of microarray data in order to identify pathological pathways. This method will complement analysis methods that aim at detecting differentially expressed genes because for complex diseases like cancer, diabetes or Parkinson’s disease it is recognized that the intricate interconnectedness among genes is key for a functional understanding of molecular causes of these diseases.

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Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping

Connectivity mapping is a recently developed technique for discovering the underlying connections between different biological states based on gene-expression similarities. The sscMap method has been shown to provide enhanced sensitivity in mapping meaningful connections leading to testable biological hypotheses and in identifying drug candidates with particular pharmacological and/or toxicological properties. Challenges remain, however, as to how to prioritise the large number of discovered connections in an unbiased manner such that the success rate of any following-up investigation can be maximised. We introduce a new concept, gene-signature perturbation, which aims to test whether an identified connection is stable enough against systematic minor changes (perturbation) to the gene-signature. We applied the perturbation method to three independent datasets obtained from the GEO database: acute myeloid leukemia (AML), cervical cancer, and breast cancer treated with letrozole. We demonstrate that the perturbation approach helps to identify meaningful biological connections which suggest the most relevant candidate drugs. In the case of AML, we found that the prevalent compounds were retinoic acids and PPARAn external file that holds a picture, illustration, etc. Object name is pone.0016382.e001.jpg activators. For cervical cancer, our results suggested that potential drugs are likely to involve the EGFR pathway; and with the breast cancer dataset, we identified candidates that are involved in prostaglandin inhibition. Thus the gene-signature perturbation approach added real values to the whole connectivity mapping process, allowing for increased specificity in the identification of possible therapeutic candidates.

For more information about the paper please visit : 

PLoS One. 2011 Jan 31;6(1):e16382. Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping. McArt, D.G. and Zhang, S.D.

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