Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Wissensmanagement in der Bioinformatik

Current Projects

  • FONDA: Foundations of Workflows for Large-Scale Scientific Data Analysis, Collaborative Research Center 1404, DFG, 2020-2024
  • SeneSys: Senescence-based systems medicine stratification of individualized lymphome therapies, BMBF, 2019-2022
  • Beyond the Exome, Research Unit, DFG, 2019-2022
  • CompCancer: Computation in Cancer, Research Trainig Group, DFG, 2019-2023
  • HEIBRiDS: Helmholtz Einstein International Research School on Data Science, Helmholtz Society, 2018 - 2025
  • TABSIM: Table Similarity Search, DFG, 2018-2020
  • PREDICT: comPREhensive Data Integration for Cancer Treatment, BMBF, 2016-2021
  • PerSonS: Personalizing Oncology via Semantic Integration of Data, BMBF, 2016 - 2020
  • Excellence Graduate School BSIO: Berlin School of Integrative Oncology, DFG, 2012-2022





FONDA: Foundations of Workflows for Large-Scale Scientific Data Analysis



Funding: DFG

Period: 2020 - 2024

Project partner: Freie Universität Berlin, Technische Universität Berlin, Zuse Institut Berlin, Max-Delbrück-Zentrum Berlin, Heinrich Hertz Institut Berlin, Charite Berlin, Hasso-Plattner-Institut Postdam

FONDA investigates methods for increasing productivity in the development, execution, and maintenance of Data Analysis Workflows for large scientific data sets. We approach the underlying research questions from a fundamental perspective, aiming to find new abstractions, models, and algorithms that can eventually form the basis of a new class of future infrastructures for Data Analysis Workflows.

SeneSys: Senescence-based systems medicine stratification of individualized lymphome therapies



Funding: BMBF

Period: 2019 - 2022

Project partner: Charite Berlin, Max-Delbrück-Zentrum Berlin, Universität Göttingen

Personalized precision oncology aims at identifying mutations in the genome of a patient’s tumor sample that are likely to operate as oncogenic drivers and susceptible to pharmacological inhibition. In this systems-medicine-driven demonstrator project, we propose to evaluate the association of senescence features in new mathematical cluster models of diffuse large B-cell lymphoma (DLBCL), and to judge the impact of lymphoma states as mediators of resistance and predictors of outcome. Moreover, we will employ machine learning to identify and clinically exploit key vulnerabilities for novel therapies.

Beyond the Exome: Improving our understanding of the non-coding regulatory genome


Beyond the Exome Homepage: Beyond the Exome

Funding: DFG

Period: 2019 - 2022

Project partner: Charite Berlin, MDC Berlin

The DFG-funded research unit "Beyond the Exome" will improve our biological understanding of the non-coding, regulatory genome. It assembles a group of leading researchers from clinical medicine, basic sciences, and bioinformatics. It will study the associations of genetic variations in the regulatory genome and human disease, focussing on non-coding regions. It will perform basic research on gene regulation, structural and cell biology, bioinformatics, and biomedical knowledge management. Results will be implemented into user friendly software tools.

CompCancer: Computation in Cancer

CompCancer Homepage: CompCancer

Funding: DFG

Period: 2019 - 2023

Project partner: Charite Berlin, MDC Berlin

CompCancer is a PhD programme (DFG funded research training group) that focusses on computational aspects of cancer research. Contemporary cancer research generates enormous data sets characterising tumours at unprecedented molecular details. To integrate and interpret these new layers of data, computational methods are becoming central to cancer research. Increasingly, computational methods are also required for clinical oncology, as more and more patients are receiving therapies based on integrated molecular profiles.

HEIBRiDS: Helmholtz Einstein International Research School on Data Science


HEIBRIDS-LOGOHomepage: Heibrids

Funding: Helmholtz Society

Period: 2018 - 2025

Project partner: All Berlin Universities, all Berlin Helmholtz Institutes

HEIBRiDS is a doctoral research school in Data Science. In cooperation between 6 Helmholtz-Centers (AWI, DESY, DLR, GfZ, HZB and MDC) located in and around Berlin and the Einstein Center for Digital Future (ECDF) involving the Berlin univerities Charité, FU, HU, TU, and UdK, HEIBRiDS benefits from interdisciplinary projects, joint supervision and mentorship from excellent researchers. HEIBRiDS focuses on data science from medicine and geo-sciences to information technology and engineering and provides an internationally highly recognized and vivid research environment within these research areas combined with the supervision by an interdisciplinary team and a rich lecture program.

TABSIM: Table Similarity Search


TABSIM-LOGOHomepage: None yet

Funding: DFG

Period: 2018 - 2020

Project partner: HPI Potsdam

Existing table similarity measures build on simple models of table metadata, structure, and content. They are designed mainly for tables with a horizontal layout where each column represents one attribute and data values are in rows, and they cannot be easily used for tables with other structures, such as matrix tables where both rows and columns are represented by attributes and values. Moreover, they rely (in different manners) on computing with frequency values of individual words which is not sufficient to capture the semantics of table elements. The main objective of this projetc is to research methods that bring more "semantics" to table similarity measures. We expect that better TSM will significantly improve the quality of applications relying on tables, such as table similarity search and table auto completion. We will approach this problem in two ways: By learning specific word embeddings optimized to yield semantically meaningful comparisons of single tokens within tables, and by designing a particular neural network architecture addressing table normalization and table comparison in a single, trainable framework.

PREDICT: comPREhensive Data Integration for Cancer Treatment



Funding: BMBF

Period: 2016 - 2019

Project partner: Charite Berlin, Berlin Institute of Health

The central aim of the PREDICT project is to develop a software system that enables clinicians to use the large body of data on the relationships between genetic/epigenetic alterations and treatment options/success in cancer, to support (a) the rapid development of new, targeted studies whose design essentially is based on genomic features, and to (b) enable a maximally informed and structured clinical decision process. A knowledge base will be created using advanced and innovative algorithms for knowledge extraction, semantic data integration, and biomedical text , and made available to the clinical oncologist through a cancer-genomic clinical workbench based. Moreover, the knowledge base will be an essential tool to initiate and support highly targeted umbrella and basket trials in which experimental drugs are administered to a typically small group of patients chosen based on their mutation status. Finally, the knowledge base will be used to develop novel algorithms to assess the effect of drugs on a patient’s tumor depending on its mutation profile.

PerSonS: Personalizing Oncology via Semantic Integration of Data



Funding: BMBF

Period: 2016 - 2019

Project partner: Universität Tübingen, KAIROS GmbH, Leibniz-Institut für Wissensmedien, Tübingen, Universitätsklinik Tübingen, Charite Berlin

The central goal of the project is to develop a software system for providing homogeneous and intuitive access to all data relevant to therapeutic decisions. The project will enable clinicians to (a) select stratified patient cohorts based on a full semantic integration of clinical and high-throughput (HT) data and (b) to bring personalized tumor therapy to a new level through a touch-based visual analytics tool allowing intuitive access to all data – literally ‘information at your fingertips’. Key tasks to achieve these goals are conversion of free text clinical reports into structured data, automated consistent analysis of HT data, a full semantic integration of the clinical data with selected HT data, the design of a user-friendly graphical user interface for the selection of patient cohorts, and finally (particularly for the translational phase) an interactive visual analytics tool based on a multi-representational interactive touch table to conduct personalized interdisciplinary tumor conferences in order to support therapeutic decisions.

BSIO: Berlin School of Integrative Oncology



Funding: DFG Excellence Initiative

Period: 2012 - 2019

Project partner: Charite Berlin + Further partners

The BSIO offers a structured 3-year doctoral program jointly educating natural scientists and physicians/medical students in the field of integrative oncology and features excellent research conditions, a comprehensive curriculum and a broad supervision and mentoring network. With respect to its scope, the BSIO aims to bring our understanding of malignant growth to new conceptual levels, i.e. to expand the molecular, cell biological, organismic and system-mathematical research focus by utilizing advanced experimental and simulatory models to develop novel diagnostics and innovative therapeutic principles, and to make them rapidly available for clinical testing.