WorldCat Identities

Brors, Benedikt

Overview
Works: 61 works in 73 publications in 2 languages and 221 library holdings
Roles: Contributor, dgs, Other, Author
Classifications: R856, 616.994
Publication Timeline
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Most widely held works by Benedikt Brors
Biomedizinische Technik by Ute Morgenstern( )

2 editions published in 2015 in German and held by 36 WorldCat member libraries worldwide

The sixth volume in the biomedical engineering textbook series addresses the interdisciplinary field of health informatics. The book discusses its pragmatic applications in clinical information systems as well as medical bioinformatics in research
Guiding Cancer Therapy: Evidence-driven Reporting of Genomic Data by Julia Perera-Bel( )

2 editions published in 2018 in English and held by 14 WorldCat member libraries worldwide

Next Generation Sequencing (NGS) has been crucial for the breakthrough experienced by cancer genomics during the last decade. In turn, the knowledge gathered has fostered the development of targeted drugs and genomics-driven cancer treatment. Some university hospitals have built the infrastructure and invested in human resources for the implementation of NGS within precision medicine initiatives. However, the expertise required to integrate the data with available knowledge spans several disciplines; the information to decipher the clinical implications codified in the genome of a tumor is
Spektroskopische Charakterisierung einer bislang unbeschriebenen Redoxgruppe im Atmungsketten-Komplex-I by Benedikt Brors( Book )

3 editions published in 1999 in German and held by 10 WorldCat member libraries worldwide

Assessment of modeling strategies for drug response prediction in cell lines and xenografts by Roman Kurilov( )

2 editions published in 2019 in English and held by 7 WorldCat member libraries worldwide

Abstract: Despite significant progress in cancer research, effective cancer treatment is still a challenge. Cancer treatment approaches are shifting from standard cytotoxic chemotherapy regimens towards a precision oncology paradigm, where a choice of treatment is personalized, i.e. based on a tumor's molecular features. In order to match tumor molecular features with therapeutics we need to identify biomarkers of response and build predictive models. Recent growth of large-scale pharmacogenomics resources which combine drug sensitivity and multi-omics information on a large number of samples provides necessary data for biomarker identification and drug response modelling. However, although many efforts of using this information for drug response prediction have been made, our ability to accurately predict drug response using genetic data remains limited. In this work we used pharmacogenomics data from the largest publicly available studies in order to systematically assess various aspects of the drug response model-building process with the ultimate goal of improving prediction accuracy. We applied several machine learning methods (regularized regression, support vector machines, random forest) for predicting response to a number of drugs. We found that while accuracy of response prediction varies across drugs (in most of the cases R2 values vary between 0.1 and 0.3), different machine learning algorithms applied for the the same drug have similar prediction performance. Experiments with a range of different training sets for the same drug showed that predictive power of a model depends on the type of molecular data, the selected drug response metric, and the size of the training set. It depends less on number of features selected for modelling and on class imbalance in training set. We also implemented and tested two methods for improving consistency for pharmacogenomics data coming from different datasets. We tested our ability to correctly predict response in xenografts and patients using models trained on cell lines. Only in a fraction of the tested cases we managed to get reasonably accurate predictions, particularly in case of response to erlotinib in the NSCLC xenograft cohort, and in cases of responses to erlotinib and docetaxel in the NSCLC and BRCA patient cohorts respectively. This work also includes two applied pharmacogenomics analyses. The first is an analysis of a drug-sensitivity screen performed on a panel of Burkitt cell lines. This combines unsupervised data exploration with supervised modelling. The second is an analysis of drug-sensitivity data for the DKFZ-608 compound and the generation of the corresponding response prediction model. In summary, we applied machine learning techniques to available high-throughput pharmacogenomics data to study the determinants of accurate drug response prediction. Our results can help to draft guidelines for building accurate models for personalized drug response prediction and therefore contribute to advancing of precision oncology
Exploring the use of a blue pigment-producing NRPS as a tagging method to easily detect engineered NRPs by Anna Degen( Book )

2 editions published in 2019 in English and held by 6 WorldCat member libraries worldwide

Integrative analysis of omics datasets by Umut Toprak( )

2 editions published in 2019 in English and held by 5 WorldCat member libraries worldwide

Development of a high-throughput CRISPR/Cas9 based fluorescent reporter to study DNA double-strand break repair choices by Paris Roidos( )

2 editions published in 2020 in English and held by 4 WorldCat member libraries worldwide

Microarray-based approach identifies microRNAs and their target functional patterns in polycystic kidney disease by Priyanka Pandey( )

2 editions published between 2008 and 2016 in English and held by 4 WorldCat member libraries worldwide

Applying machine learning to derive actionable insights in precision oncology by Mi Yang( )

1 edition published in 2019 in English and held by 4 WorldCat member libraries worldwide

High-confidence fusion gene detection in different tumor entities & biomarker discovery in breast cancer by Zhiqin Huang( )

1 edition published in 2017 in English and held by 4 WorldCat member libraries worldwide

The fate of RNA and RNA binding proteins in Sindbis virus infection by Shuai Ni( )

2 editions published in 2020 in English and held by 3 WorldCat member libraries worldwide

Analysis of epigenetic deregulation in pediatric pilocytic astrocytoma and pineoblastoma by Christian Felix Aichmüller( Book )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Molecular classification of pediatric brain tumor entities using DNA methylation profiling by Tanvi Sharma( Book )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

TelomereHunter - in silico estimation of telomere content and composition from cancer genomes by Lars Feuerbach( )

2 editions published in 2019 in English and held by 3 WorldCat member libraries worldwide

 
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Audience Level
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Audience level: 0.90 (from 0.83 for Biomedizin ... to 0.97 for Biomedizin ...)

WorldCat IdentitiesRelated Identities
Alternative Names
Benedikt Brors researcher

Benedikt Brors wetenschapper

Languages
English (20)

German (11)