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b-it Lecture Series in Summer Semester 2016

B-IT professors organise a lecture series in Summer Semester 2015. This summer semester it is organised by:

  • Professor Dr. Jürgen Bajorath (Life Science Informatics)
  • Professor Dr. Christian Bauckhage (Media Informatics)
  • Professor Dr. Hofmann-Apitius (Life Science Informatics)
  • Dr. Holger Fröhlich (UCB, formerly B-IT)

Venue : B-IT Lecture hall 
Time : 17.00 hours 

Integrating multimodal big data to understand human disease

30th June 2016

Dr. Stefan Bonn German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany

Abstract

It is conceivable that in the not too distant future we will obtain detailed information on the genomic, epigenomic, proteomic, and metabolomic footprints of a human being. This huge amount of quantitative data will enable us to understand human functionality in health and disease with unprecedented detail, entering a potential 'Golden Age of Bioinformatics'. In my talk I will highlight the current status of data integration and analysis in bioinformatics setting and the challenges that we face. I'll explain this by taking a look at research projects of our laboratory, ranging from the detection of biomarkers to mechanistic insights into human disease.

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Diagnosing, monitoring & developing models of disease using complex biological and data

9th June 2016

Dr. Angela Hodges King's College London, United Kingdom 

Abstract

Technological advances have facilitated unprecedented scope for highly sensitive in-depth biological measurements. All manner of clinical, biological and genetic data can be generated on an enormous collective scale and applied to specific biological and translational questions. Inevitably, there is noise, false assumptions and stochastic factors leading to false dawns. In time, greater data, refined analytical methods and knowledge will overcome some of these problems and increase the number of real-life impacts using this highly complex data. Using examples, this lecture will explore how complex data has and is being used to achieve specific goals in the field of neurodegenerative disease.

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Integrative strategies and bioinformatics tools in biomedical research

2nd June 2016

Professor Dr. Ferran Sanz Research Programme on Biomedical Informatics (GRIB) Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra, Barcelona, Spain

Abstract

The integrative analysis of the Biomedical Big Data (BBD) offers new opportunities for understanding the complex basis of diseases and, consequently, for designing better treatments for them. This BBD is constituted by information resulting from biological and pharmaceutical research (‘omics information, knowledge contained in the biomedical literature, etc.), and data generated in the clinical practice (electronic health care records, medical imaging, etc.). Since most of this information is stored in not-structured formats, the computational techniques for automatic knowledge retrieval (e.g. text-mining) are paramount. This presentation includes several examples of integrative analyses of the BBD, such as the biological substantiation pipeline [1] developed in the framework of the EU-ADR Alliance, a collaborative framework for drug safety studies , as well as several analyses on disease commorbities [2,3] carried out using the DisGeNET [4] and PsyGeNET [5] resources on gene-disease associations. Regarding the application of integrative strategies in pharmaceutical R&D, the Open PHACTS on semantic web-based integrative knowledge management, and the eTOX project [6,7] on drug toxicity prediction will be presented.

[1] Bauer-Mehren A, van Mulligen EM, Avillach P, Carrascosa MC, Garcia-Serna R, Piñero J, Singh B, Lopes P, Oliveira JL, Diallo G, Ahlberg Helgee E, Boyer S, Mestres J, Sanz F, Kors JA, Furlong LI. Automatic Filtering and Substantiation of Drug Safety Signals. PLoS Comput Biol 2012; 8(4). 

[2] Grosdidier S, Ferrer A, Faner R, Piñero J, Roca J, Cosío B, Agustí A, Gea J, Sanz F, Furlong LI. Network medicine analysis of COPD multimorbidities. Resp Res 2014; 15:111. 
[3] Faner R, Gutiérrez-Sacristán A, Castro-Acosta A, Grosdidier S, Gan W, Sánchez-Mayor M, Lopez-Campos JL, Pozo-Rodriguez F, Sanz F, Mannino D, Furlong LI, Agustí A. Molecular and clinical diseasome of comorbidities in exacerbated COPD patients. Eur Respir J 2015; 46:1001–10. 
[4] Piñero J, Queralt-Rosinach N, Bravo À, Deu-Pons J, Bauer-Mehren A, Baron M, Sanz F, Furlong LI. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford) 2015; 1–17. 
[5] Gutiérrez-Sacristán A, Grosdidier S, Valverde O, Torrens M, Bravo A, Piñero J, Sanz F, Furlong LI. PsyGeNET: a knowledge platform on psychiatric disorders and their genes. Bioinformatics 2015; 31(18):3075–77. 
[6] Cases M, Briggs K, Steger-Hartmann T, Pognan F, Marc P, Kleinöder T, Schwab CH, Pastor M, Wichard J, Sanz F. The eTOX data-sharing project to advance in silico drug-induced toxicity prediction. Int J Mol Sci 2014; 15:21136-54. 
[7] Obiol-Pardo C1, Gomis-Tena J, Sanz F, Saiz J, Pastor M. A multiscale simulation system for the prediction of drug-induced cardiotoxicity. J Chem Inf Model 2011; 51:483-92.

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Computational models to understand and combat cancer: from clinical genomics to biochemical modelling

14th April 2016 

Professor Dr. Julio Saez-Rodriguez RWTH Aachen, Joint Research Center for Computational Biomedicine, Aachen, Germany

Abstract

Large-scale genomic studies are providing unprecedented insights into the molecular basis of cancer, but it remains challenging to leverage this information for the development and application of therapies. We have performed an integrated analysis of the molecular profiles of 11,215 primary tumours and 1,001 cancer cell lines, along with the response of the cell lines to 265 anti-cancer compounds. This analysis finds alterations in tumours that can confer drug sensitivity or resistance, and sheds light on which data types (genomic, transcriptomic, or methylation) are most informative to prioritize treatment. 
Integration of this data with various sources of prior knowledge, such as signaling pathways, points at molecular processes involved in resistance mechanisms. These mechanisms are often poorly understood, and mathematical mechanistic models can be built to dissect biochemically the mechanism of action of targeted therapeutics, and understand the molecular basis of drug resistance, thereby providing new treatment opportunities. 

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From Data to Decisions: - How can Computation Drive Drug Discovery?

12th April 2016

Dr. Pat Walters Vertex Pharmaceuticals, Boston, USA 

Important notice: This lecture will begin at 16:00 hours (4.00 pm)

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