I work in the System Managers team and currently support the reporting application CART. I am also responsible for the reporting part of Gemstone and currently working on the reports needed for MVP-2. I am located in the UK and have worked at Bayer for 15 years.

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Understanding how compounds affect the disease biology of patients, and which potential side effects could be expected, is one of the core challenges in drug discovery. Transcriptomics (RNA-seq) allows us to measure these compound effects via an unbiased readout of gene expression changes. While „conventional“ analysis of RNA-seq data yields insights into individual compound effects and similarities, deeper connections between principles of compound structure and transcriptome effect elude us. Insights into this connection would help us support project teams with data-driven decision making on compounds. In this pilot, we are bringing together a cross-functional team of colleagues from LST, CMD, SyMOL and DS&AI to tackle this challenge and build a toolkit to enable understanding of ​how compound structure relates to complex gene expression patterns​ and to explore common themes amongst structures or shared off-target/general perturbation effects. This toolbox will become the starting point for other efforts aimed at leveraging multi-omics data in understanding compound mechanism of actions. Artwork created using Stable Diffusion.

Given the considerable amount of data that is collected throughout clinical trials, it can be asked if this data can be used to improve our understanding of the trial, regardless of outcome. The radiomics pilot seeks to provide a toolkit that is capable of analyzing the different modalities of information found in clinical trials (genome, gene expression, biomarker, clinical information, and imaging information) to derive insights into why a patient may be responding, or why a clinical trial was unsuccessful. The current efforts have focused on clinical trials in oncology, while collaborating with the RED-ONC function. Overall, the project is in its pilot state, and demonstrating a proof of concept, while documenting many challenges and learnings with the development of such a toolkit. Potential directions that could expand this toolbox include deriving the factors that best predict why a patient would stay in a clinical trial, and providing assistance with patient selection of clinical trials. Other potential directions involve suggesting why a particular trial may have been unsuccessful and providing that information back to the drug development team.

Professional with extensive experience in analyzing, designing, and implementing novel solutions through the use of Machine Learning for its applications in Research and Development for the Healthcare and Pharmaceutical Industry. I employ my interpersonal communication, business, and technology integration talents to set product strategy and create product roadmaps in an agile environment. • Talent Management • Team-Player • Effective Communicator • Detail Oriented • Analytical Mindset • Computer Vision • Machine Learning • Natural Language Processing • Data Analysis • Statistical Modeling

The Data Assets and Insights Solutions subfunction, DAIS, consists of a team of dedicated computer and data scientists which combine deep R&D process and domain expertise (biology, chemistry, pharmacy, etc) with advanced computational expertise (software engineering, natural language processing, scalable architectures, bio- & chem-informatics, data integration & visualization, etc). We integrate, enrich and harmonize large scientific datasets to build foundational data assets which are used by the R&D organization and our team to build insights solutions. They address key R&D challenges to improve speed, quality and probability of success of our R&D pipeline. Selected business critical tasks within R&D we are focusing on: - Target identification including holistic assessment - Disease understanding including precision medicine - Pipeline evaluation (therapeutic potential and risks) - Modality selection including identification of tool compounds - Identification of collaboration and in-licensing opportunities For more information contact: Wolfgang Thielemann. Artwork: Licensed pictures.