References of "Lengauer, Thomas"
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See detailMaking sense of big data in health research: Towards an EU action plan.
Auffray, Charles; Balling, Rudi UL; Barroso, Ines et al

in Genome medicine (2016), 8(1), 71

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation ... [more ▼]

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans. [less ▲]

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See detailPredicting MHC class I epitopes in large datasets
Roomp, Kirsten UL; Antes, Iris; Lengauer, Thomas

in BMC Bioinformatics (2010), 11(90), 1-2

BACKGROUND: Experimental screening of large sets of peptides with respect to their MHC binding capabilities is still very demanding due to the large number of possible peptide sequences and the extensive ... [more ▼]

BACKGROUND: Experimental screening of large sets of peptides with respect to their MHC binding capabilities is still very demanding due to the large number of possible peptide sequences and the extensive polymorphism of the MHC proteins. Therefore, there is significant interest in the development of computational methods for predicting the binding capability of peptides to MHC molecules, as a first step towards selecting peptides for actual screening. RESULTS: We have examined the performance of four diverse MHC Class I prediction methods on comparatively large HLA-A and HLA-B allele peptide binding datasets extracted from the Immune Epitope Database and Analysis resource (IEDB). The chosen methods span a representative cross-section of available methodology for MHC binding predictions. Until the development of IEDB, such an analysis was not possible, as the available peptide sequence datasets were small and spread out over many separate efforts. We tested three datasets which differ in the IC50 cutoff criteria used to select the binders and non-binders. The best performance was achieved when predictions were performed on the dataset consisting only of strong binders (IC50 less than 10 nM) and clear non-binders (IC50 greater than 10,000 nM). In addition, robustness of the predictions was only achieved for alleles that were represented with a sufficiently large (greater than 200), balanced set of binders and non-binders. CONCLUSIONS: All four methods show good to excellent performance on the comprehensive datasets, with the artificial neural networks based method outperforming the other methods. However, all methods show pronounced difficulties in correctly categorizing intermediate binders. [less ▲]

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See detailHLA class I allele associations with HCV genetic variants in patients with chronic HCV genotypes 1a or 1b infection
Lange, Christian Markus; Roomp, Kirsten UL; Dragan, Anette et al

in Journal of Hepatology (2010), 53(6), 1022-1028

BACKGROUND & AIMS: The adaptive immune response against hepatitis C virus (HCV) is significantly shaped by the host's composition of HLA-alleles with the consequence that the HLA phenotype is a critical ... [more ▼]

BACKGROUND & AIMS: The adaptive immune response against hepatitis C virus (HCV) is significantly shaped by the host's composition of HLA-alleles with the consequence that the HLA phenotype is a critical determinant of viral evolution during adaptive immune pressure. In the present study, we aimed to identify associations of HLA class I alleles with HCV subtypes 1a and 1b genetic variants. METHODS: The association between HCV genetic variants and specific HLA-alleles was investigated in a cohort of 159 patients with chronic HCV genotypes 1a- and 1b-infection who were treated with pegylated interferon-alfa 2b and ribavirin in a prospective controlled trial for 48 weeks by direct sequencing of the genes encoding the HCV proteins E2, NS3, and NS5B and by HLA class I-genotyping of patients. HCV genetic variants were associated with specific HLA-alleles and the binding strength of accordant amino acid sequences to the corresponding HLA-allele was assessed by using the SYFPEITHI-algorithm. RESULTS: Overall, associations between HLA class I alleles and HCV sequence variation were rare. Five unknown HLA class I-associated viral genetic variations were identified, which in part affected the binding of predicted HCV CD8+ T cell epitopes to the respective HLA-allele. In addition, different patterns of HLA class I-allele/HCV sequence associations between the two subtypes were observed. CONCLUSIONS: We identified several unknown HLA class I-restricted HCV variants which in part impair binding to predicted HCV CD8+ T cell epitopes with remarkable differences between HCV subtypes 1a and 1b quasispecies. [less ▲]

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See detailArevir: A Secure Platform for Designing Personalized Antiretroviral Therapies Against HIV
Roomp, Kirsten UL; Beerenwinkel, Niko; Sing, Tobias et al

in Leser, U.; Naumann, F.; Eckman, B. (Eds.) Data Integration in the Life Sciences (2006)

Despite the availability of antiretroviral combination therapies, success in drug treatment of HIV-infected patients is limited. One reason for therapy failure is the development of drug-resistant genetic ... [more ▼]

Despite the availability of antiretroviral combination therapies, success in drug treatment of HIV-infected patients is limited. One reason for therapy failure is the development of drug-resistant genetic variants. In principle, the viral genomic sequence provides resistance information and could thus guide the selection of an optimal drug combination. In practice however, the benefit of this procedure is impaired by (1) the difficulty in inferring the clinically relevant information from the genotype of the virus and (2) the restricted availability of this information. We have developed a secure platform for collaborative research aimed at optimizing anti-HIV therapies, called Arevir. A relational database schema was designed and implemented together with a webbased user interface. Our system provides a basis for monitoring patients, decision- support, and computational analyses. Thus, it merges clinical, diagnostic and bioinformatics efforts to exploit genomic and patient therapy data in clinical practice. [less ▲]

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