Prediction of Drug Induced Gene Expression Perturbations through Drug Target and Protein-Protein Interaction Information
Background Transcriptomics provide insights into gene expression and with it the ability to analyze one of the fundamental processes of life - the translation from gene to protein. Single Cell RNA sequencing (scRNAseq) is a technology that measures transcriptomics on the single cell level. However, biological data is highly complex, variability and noisy, making it challenging to analyze and work with. The goal of the project is to evaluate if deep learning can infer gene expression profiles of specific conditions (exposures) by only receiving prior information about an exposure, such as a drug’s known gene targets as well as a general protein-protein interaction network. The aim is to evaluate the model based on its zero-shot performance (e.g. unseen drugs). ...