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b-it Lecture Series "Algorithms in Bioinformatics" Winter Semester 2011/2012

b-it Professor Holger Fröhlich organises a new lecture in Winter Semester 2011/2012. It is the lecture series "Algorithms in Bioinformatics". All lectures will be held in b-it Rheinsaal at 17.00 hours (5.00 pm). 

Network Bioinformatics to Fight Infectious Diseases

20.10.11 

Dr. Mario Albrecht Max-Planck-Institute of Computer Science, Computational Biology and Applied Algorithmics, Germany 
More information about the lecturer

Abstract 

Standard disease therapies are often not as effective as desired and cause unwanted side effects for patients. Therefore, medical researchers apply new high throughput technologies to gain insight into the cellular disease processes and to discover novel therapeutic options and drug target molecules. To this end, innovativebioinformatics approaches to systems medicine are required to explore the huge and ever-growing amounts of molecular network data for complex diseases. Large data volumes need to be processed, integrated, analyzed, and interpreted by efficient information systems and methods to advance the understanding how molecular networks drive biological processes and how network perturbations link to diseases. In particular, this talk will also highlight how to block the viral life cycle based on integrative network models.

Investigating the dynamics of chemical reaction systems using computer algebra software

15/12/2011 

Professor Dr. Francois Boulier University Lille I France Laboratoire d'Informatique Fondamentale de Lille, France

Abstract

Chemical reaction systems provide a framework which permits to model phenomenons not at all restricted to chemically reacting substances. They may be endowed with many different dynamics. They are widely used in systems biology. In this talk, I will show how computer algebra software may help studying these systems and their dynamics. The talk will be illustrated with various examples, carried out on the MAPLE computer algebra system.

Probabilistic data integration for functional genomics: from cancer studies to human microbiomics

19/01/2012 

Dr. Leo Lahti Helsinki University of Technology, Finnland and University of Wageningen, the Netherlands
More information about the lecturer

Abstract

Combining evidence from heterogeneous measurement sources is a prerequisite for obtaining holistic understanding of genomic systems and their dynamic regulation under environmental influences. High levels of uncontrolled variation combined with relatively small sample size and limited prior knowledge set considerable challenges to the analysis of high-dimensional genomic observations. Recent advances in probabilistic data integration methodology and open access to research data and algorithms help to address some of these challenges. The role of transparent data integration models in contemporary genome research is highlighted with recent applications in cancer transcriptomics and human-associated microbial ecosystems. 

Kernel methods for genomic data fusion

02/02/2012

Prof. Dr. Yves Moreau Katholieke Universiteit Leuven, Belgium

Abstract

Despite significant advances in omics techniques, the identification of genes causing rare genetic diseases and the understanding of the molecularnetworks underlying those disorders remains difficult. Gene prioritization attempts to integrate multiple, heterogeneous data sources to identifycandidate genes most likely to be associated with or causative for a disorder. Such strategies are useful both to support clinical genetic diagnosis and to speed up biological discovery. Genomic data fusion algorithms are rapidly maturing statistical and machine learning techniques have emerged that integrate complex, heterogeneous information (such as sequence similarity, interaction networks, expression data, annotation, or biomedical literature) towards prioritization, clustering, or prediction. In this talk, we will focus in particular on kernel methods and will propose several strategies for prioritization and clustering in particular. We alsogo beyond learning methods as such by addressing how such strategies canbe embedded into the daily practice of geneticists, mostly through collaborative knowledge bases that integrate tightly with prioritization and network analysis methods. 

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