Recent Posts

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The first MOLIT Symposium is actively promoting the dialogue of implementing personalized medicine into treatment routine. Therefore, a platform to interlink practicing physicians and IT experts is formed. This opens up possibilities to join forces between oncology and IT on problems in patient care to make solutions usable in everyday clinical practice.

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3 min read

We are looking for skilled PhD students and Postdocs to contribute in building a central IT platform for gut microbiome research. On the medical informatics side, we seek expertise to build up a medical data integration platform that enables harmonization and analysis of sensitive data while adhering to data protection requirements. On the bioinformatics side, we seek expertise to build and integrate pipelines for systematic analysis of heterogeneous experimental data ranging from 16S ribosomal RNA to complex metabolome profiles. Moreover, we seek expertise for developing and integrating state-of-the-art statistical and network analysis methods into the platform. These methods will facilitate knowledge extraction, biomarker discovery and hypothesis generation for the CRC.

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Recent & Upcoming Talks

Big Data als Wegbereiter der Präzisionsmedizin
08. Mar 2019 14:30
Jahressymposium der IGLD 2019
Overcoming challenges in the integrative analysis of epigenomic data
11. Feb 2019 11:30
ICB Seminar
Project
Genome-wide endogenous RNA networks highlight novel biomarkers in cancer
29. Jan 2019 17:00
Seminar Institut für Medizinische Mikrobiologie, Klinikum Rechts der Isar
Project
Analysing large-scale epigenomic data with DeepBlue
06. Dec 2018 14:30
European Bioconductor Meeting 2018
Project
Genome-wide endogenous RNA networks highlight novel biomarkers in cancer
28. Sep 2018 15:00
Guest Lecture at the SFB-54: ‘InThro’
Project

Selected Publications

Here we used single-cell RNA-seq data of ~11,000 cells from the tumor microenvironment to simulate bulk samples of known cell type proportions to measure the accuracy of computational methods for cell-type deconvolution and quantification.
bioRxiv preprint, 2018
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We present a novel method for assessing the performance of batch effect adjustment methods on heterogeneous data. Our method borrows information from the Cell Ontology to establish if batch adjustment leads to a better agreement between observed pairwise similarity and similarity of cell types inferred from the ontology.
Bioinformatics (ECCB 2018 conference proceedings), 2018
PDF Code DOI

Recent Publications

More Publications

. CFP suppresses breast cancer cell growth by TES-mediated upregulation of the transcription factor DDIT3. Oncogene, 2019.

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. Comprehensive evaluation of cell-type quantification methods for immuno-oncology. bioRxiv preprint, 2018.

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. An ontology-based method for assessing batch effect adjustmentapproaches in heterogeneous datasets. Bioinformatics (ECCB 2018 conference proceedings), 2018.

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. MiRNA-181d Expression Significantly Affects Treatment Responsesto Carmustine Wafer Implantation. Neurosurgery, 2018.

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. JAMI - Fast computation of Conditional Mutual Information forceRNA network analysis. Bioinformatics, 2018.

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. The long non-coding RNA H19 suppresses carcinogenesis and chemoresistance in hepatocellular carcinoma. Cell Stress, 2017.

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. De novo pathway-based biomarker identification. Nucleic Acids Res., 2017.

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. Interactive microbial distribution analysis using BioAtlas. Nucleic Acids Res., 2017.

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. Using Docker Compose for the Simple Deployment of an Integrated Drug Target Screening Platform. J. Integr. Bioinform., 2017.

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. Systematic screening of isogenic cancer cells identifies DUSP6 as context-specific synthetic lethal target in melanoma. Oncotarget, 2017.

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Projects

We develop tools that use large-scale paired miRNA and gene expression data to infer microRNA regulation networks driven by the competing endogenous RNA effect. SPONGE is based on partial correlations while JAMI is based on conditional mutual information.

We develop tools that leverage information from molecular interaction networks in understanding molecular profiling data. De novo network enrichment tools operate by using heuristics to extract subnetworks that are relevant for a phenotype of interest, e.g. a disease.

FeatureCloud

The EU H2020 project FeatureCloud aims at developing methods for privacy-preserving, federated machine learning.

We developed a series of web applications that operate as an integrated high-throughput screening platform. The platform covers the entire research chain from sample management, HTS logistics, data processing and normalization, hit (drug target) discovery and systems-biology-driven evaluation.

The DeepBlue web application server and its accompanying bioconductor R package facilitate user-friendly programmatic access to publicly available big epigenomic data sets.

The EU H2020 project REPO-TRIAL aims at developing an in silico approach to optimise the efficacy and precision of drug repurposing trials. To this end we integrated heterogeneous data into a comprehensive interactome of disease-drug-gene interactions (a new diseasome).

Teaching

SS 2018

  • Einführung in die Bioinformatik II / Introduction to Bioinformatics II

WS 2018⁄2019

  • Einführung in die Bioinformatik I
  • Journal Club Bioinformatics

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