Visual Analytics for Enhancing Quality and Trust in Genome-wide Expression Clustering and Annotation

Among the researchers in life sciences, there is a need for a functional genome-wide annotation of the protein-coding genes to get a deeper understanding of mammalian biology. Present genome-wide annotation tools are useful, but require arbitrary cut-offs, commonly obtained via black-box computational models. This may hinder the ability of the analyst to make an informed decision regarding what are relevant fold-changes and detection limits for the underlying transcriptomics data.

The aim of this interdisciplinary project is to develop a new data-driven strategy for exploring whole-body co-expression patterns, using interpretable machine learning with the help of interactive visualization techniques, that support informed decisions, leading to better predictions and improved trustworthiness of the results.

This project is performed in cooperation with the colleagues from SciLifeLab/KTH (led by Prof. Dr. Mathias Uhlén). It is funded as part of the WASP (Wallenberg AI, Autonomous Systems and Software Program) and DDLS (the SciLifeLab and Wallenberg National Program on Data-Driven Life Science) joint research projects launched in 2022.