I studied piano performance and toxicology, and am now Master Data Manager at Bayer Pharma in the R&D Master Data Management team of Data Science & AI. I am passionate about team collaboration and rowing. Currently, I'm co-leading the IDMP Ontology, an international cross-industry project with 12 pharma companies for the unique identification of medicinal products to ensure patient safety. Talk to me about common standards and high quality data for better ML and AI outcomes, our next collaboration project, and/or our next jam session at the Bayer Berlin campus.
My name is Pinky Damani, I'm with Bayer for 14yrs, originally from Mumbai, India, but settled in NJ, USA. I love to play cricket and badminton. Married, 2 children. Currently, working on Spectrum DMW platform with CDSA, Accenture and Oracle, part of Data Transformation and Ingestion Team, I'm very excited and happy to be part of DSA&I. Looking forward to our connect!
Our vision at Data Science Services & Solutions, DS3, is to Enable, Scale, and Operationalize R&D Data Science & AI capabilities to achieve Clinical Research and Development Speed, Efficiency, and Optimization. Learn more about our areas of responsibility, capabilities, strategic priorities, and collaborations. For more information, contact DS3 Head: Abi Velurethu. 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.
The R&D Digital Fluency Program (DFP) is supporting you with learning offerings to build up competencies and further skillsets around digital technologies to enable a competitive and digitally literate organization. At the Digital Fluency Program, we set ourselves apart by offering learning opportunities created by your peers who understand the unique challenges you face in your job. Our tailored approach focuses on real-life use cases, taking our training far from generic and providing you relevant and impactful skill development. In 2020 we, a small team of four people, started our journey with the DFP as part of the R&D Digital Roadmap. Since technologies are ever evolving and impacting our work environment it is more important than ever to foster a culture of continuous learning and enable future opportunities. Thus the DFP learning portfolio includes something for everyone, regardless of your level of knowledge: It offers general awareness programs to enhance basic digital literacy, self-learning pathways on data analysis & visualization and the ability to access online courses like Coursera via the Digital Curriculum. The team ensured the learning objective was met by piloting newly developed learning programs, making use of cross-functional development, and incorporated R&D specific components prior to a broader roll-out in the organization.
Morphological profiling with Cell Painting allows an unbiased characterization of cellular states by observing changes in cell morphology. Cell Painting images are information-rich and can guide the elucidation of mode of action, toxicity, and off-target effects, help drug repurposing, indicate differentiation states of iPSC lines, discover new biology and more. However, this technology is data-greedy and computationally complex and demanding. At DS&AI, we enable this technology at Bayer! To this end, we support large data generation campaigns internally and externally, develop novel machine learning algorithms, and create tools for efficient analysis and visualization of Cell Painting data. For more information please contact Paula Marin Zapata or Marc Osterland. Self-painted image modified using pictures from https://quizlet.com/420027050/animal-cell-diagram/ as starting models.
Position: Sr. Manager, Value & Insights Team: Value, Engagement & Training (VET) Dep.: Product Owner Hub (POH) Location: Wuppertal, Germany
