Personalization in oncology – Prediction of cancer therapy effectiveness from Omics data through structural modeling and AI

Mentored by Kristin Reiche & Jens Meiler
at ScaDS.AI, Fraunhofer IZI and Institute of Drug Discovery, Leipzig University

New cancer therapies, including CAR T cell therapies, have shown surprising effectiveness against many cancer types but fail sometimes without any clearly observable reason for other patients. Data from transcriptomics and new deep learning methods for structural modeling are available at this point in time, but the mapping of observed genetic differences to structural interactions and subsequently predictions on therapy effectiveness and safety are only sparsely conducted. In this project, Kristin Reiche, an expert on transcriptomics data analysis, and Jens Meiler, a computational structural biologists and artificial intelligence expert, come together to establish a comprehensive pipeline to map genetically observed differences in cancer therapy targets through structure prediction to clinically observed effects.

Work environment

You will work at the Interdisciplinary Center for Bioinformatics in Leipzig and cooperate with the machine learning and AI experts at the scaDS.AI in Leipzig and Dresden, the Fraunhofer Institute for Cell Therapy and Immunology and the Institute of Drug Discovery at Leipzig University. You will have access to machine-learning HPC resources of the ScaDS.AI Center for Scalable Data Analytics and AI Dresden/Leipzig, offering state-of-the-art CPU (Intel and IBM POWER 9) and GPU (Nvidia A100 and V100) resources, as well as high-performance computing and storage systems for computational experiments.

Prerequisites

The candidate will need a solid background in bioinformatics or structural biology as well as proficiency in scientific programming and interest in data analysis. Prior expertise in machine learning, genomics or transcriptomics, and AI techniques will be an advantage.