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Monday, 23.1.2023, 14:00-17:30

Berlin-Mitte, Philipstr. 13, Haus 18 (Leonor Michaelis Haus), 3rd floor, Maud Menten Hall

Programme:

14.00   Ulf Leser: Welcome
14.10   Dieter Beule: Local and national systems for omics data mangement
14.50   Bert Arnrich: Connected Healthcare: Paving the Way for a User-Centered and Preventive Healthcare Model
15.30   Break
16.00   Meike Zehlike: Beyond Incompatibility in Algorithmic Fairness -- A FAir Interpolation Method
16.40   Fabian Praßer: Sharing data for translational research: privacy-enhancing approaches
17.20   Wrap Up
17:30  End of workshop

 

Dieter Beule: Local and national systems for omics data mangement
Scientists employing large-scale omics in life science research face challenges in modeling of multi-assay studies, recording of relevant parameters, and managing resulting large data volumes with complex metadata. We introduce SODAR, the system for omics data access and retrieval, that helps to address some of these challenges. Furthermore, we discuss emerging national infrastructures for sharing data omics data across sites.

Bert Arnrich: Connected Healthcare: Paving the Way for a User-Centered
and Preventive Healthcare Model

Connected Healthcare aims to pave the way for transforming healthcare systems from purely managing illness to maintaining wellness. Ubiquitous sensing and computing technologies are foreseen as the key enabler for pushing the paradigm shift from the established provider-centric healthcare model to a user-centered and preventive overall lifestyle health management that is available everywhere, anytime and to anyone. In this talk we will provide examples of the three pillars of success: unobtrusive sensing technology, adequate data processing and modeling, and persuasive human-computer interfaces.

Meike Zehlike: Beyond Incompatibility in Algorithmic Fairness -- A FAir Interpolation Method

Trustworthy AI becomes ever more important, both in machine learning and in the law. One important consequence is that decision makers must seek to guarantee a `fair', i.e., non-discriminatory, algorithmic decision procedure. However, there are several competing notions of algorithmic fairness that have been shown to be mutually incompatible under realistic factual assumptions. This concerns, for example, the widely used fairness measures of ‘calibration within groups’ and ‘balance for the positive/negative class’. Indeed, the COMPAS algorithm, which predicts recidivism risk of criminal offenders, exhibits racial bias according to the balance metrics, but not regarding calibration.
In this talk, I present a novel algorithm (FAIM) for continuously interpolating between these three fairness criteria. Thus, an initially unfair prediction can be remedied to at least partially meet a desired, weighted combination of the respective fairness conditions. The algorithm relies on methods from the mathematical theory of optimal transport. We demonstrate the effectiveness of our algorithm when applied to synthetic data, the COMPAS data set, and a new, real-world data set from the e-commerce sector. Finally, I discuss to what extent FAIM can be harnessed to comply with conflicting legal obligations. The analysis suggests that it may operationalize duties in traditional legal fields, such as credit scoring and criminal justice proceedings, but also for the latest AI regulation put forth in the EU, like the recently enacted Digital Markets Act.

Fabian Praßer: Sharing data for translational research: privacy-enhancing approaches

Data sharing and the re-use of health data for secondary purposes have become a core elements of biomedical research, resulting in complex privacy challenges. Ideally, generic solutions would allow researchers to integrate and process biomedical data on a large scale, while citizens and patients can exercise their right to privacy and control what happens to their personal data. However, there are inherent trade-offs and technical solutions typically require balancing conflicting objectives: (1) privacy protection and (2) usefulness. Both objectives can be broken down into several further aspects. First, the term "privacy protection" can relate to a wide variety of different guarantees or degrees of control that can be provided. Second, the term "usefulness" may refer to the quality of data or results, to the flexibility with which users can perform analyses or to the scalability of platforms. In this talk, we will provide an overview of different properties that translational research data platforms can have and analyze common approaches.

For the seminar on the 7th of December at 11 am Sofya Marchenko invited Pauline Hiort (PhD student from Prof. Dr. Bernhard Renard Group at the HPI) to talk about her research. If you are interested in joining write an e-Mail to compcancer at charite dot de to receive the zoom link.

DrDimont: explainable drug response prediction from differential analysis of multi-omics networks

The recent publication explained a novel method for condition-specific molecular networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks, which allows to predict drug responses. To demonstrate the efficiency the method, it is applied to breast cancer patients’ samples containing transcriptomics, proteomics, phosphosites and metabolomics measurements and compares drug response between estrogen receptor positive and receptor negative patients. 

The paper

For the seminar on the 2nd of December at 4 pm Robin Xu invited Leif Ludwig (MDC/BIH) to talk about his research. If you are interested in joining write an e-Mail to compcancer at charite dot de to receive the zoom link.

From human lineage tracing to mitochondrial genetics.

Abstract:

Somatic mutations enable lineage and clonal tracing of human cells to reconstruct cellular population dynamics. Here, I will review our efforts to leverage somatic mitochondrial DNA mutations and single-cell multi-omics for studying clonal dynamics in human malignancies, as well as in the innate immune system and more broadly hematopoiesis. Further, I will discuss how mitochondrial genetic variation contributes to cellular heterogeneity and human phenotypes, including in the immune system and cancer. For example, we reveal pathogenic variants to be selected against in T cell subsets, suggestive of distinct metabolic vulnerabilities of human immune cells.

Bio:

Leif S. Ludwig graduated with a Master of Science (Diploma) and PhD (Dr. rer. nat.) in Biochemistry from the Freie Universität Berlin and MD from the Charite Universitätsmedizin Berlin. During his PhD he worked in the laboratory of Harvey Lodish at the Whitehead Institute for Biomedical Research, functionally investigating how human genetic variation affects human traits and phenotypes, in particular in the context of congenital blood disorders. During his postdoc in the laboratories of Aviv Regev and Vijay Sankaran at the Broad Institute of MIT and Harvard he established single-cell sequencing approaches leveraging natural mitochondrial sequence variation to enable the clonal tracing of human cells in a physiologic human context. In November 2020, he started his Emmy Noether research group at the Berlin Institute of Health at Charité and Berlin Institute for Medical Systems Biology at the MDC, where he and his team develop and apply single-cell technologies to investigate fundamental properties of mitochondrial genetics and stem cell dynamics in human hematopoiesis.

Professor Itai Yanai is the Director of the Institute for Computational Medicine and Professor of Biochemistry and Molecular Pharmacology at NYU Grossman School of Medicine. His research focus is mainly on gene expression profiling and transcriptome. He will be our next speaker on 16.11.2022 at 3pm Berlin Time. If you are interested in joining write an e-Mail to compcancer at charite dot de to receive the zoom link.

Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment

Goal:
Propose a unified catalog of gene modules that could underpin the recurrent cancer cell states by using scRNA-Seq, spatial transcriptomics and CODEX over 15 cancer types.

Abstract:
Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.

For the seminar on the 19th of October Sinduja Chandrasekaran invited Jan Koster to talk about the R2 tool. If you are interested in joining write an e-Mail to compcancer at charite dot de to receive the zoom link.

R2: Genomics Analysis and Visualization Platform guided by stories from the neuroblastoma research

Overall goal

Development of a dedicated bioinformatics platform, loaded with tools and methods which allow wetlab biologists to interpret their own high-throughput experiments (gene expression data, chip-seq, whole genome sequencing, whole exome, methylation, etc ). 

Reaching this goal

R2 is our in house developed web-based bioinformatics analysis and visualization platform that enables analysis of probable targets in the context of (tumor) gene expression data (url: http://r2.amc.nl). Specifically focusing at biologists and biomedical scientists, R2 aids in the analysis and visualization of gene expression data (tumor series, experiments) and their annotated features (such as clinical information). R2 contains an expanding set of analyses which are heavily inter-connected, allowing users to quickly hop from one view (representation of the data) to another. Currently R2 supports all major platforms for human, mouse and rat and harbors more than 1.000.000 public samples (divided over more than 1800 datasets) and in addition also restricted datasets from research groups and consortia. R2 is also being employed in the integration, analysis and visualization of copy numbe / ChIP-seq / Whole genome sequence information in a wide array of research collaborations that include both research groups from around the world, as well as consortia in which we actively participate.