Interpretable deep learning for cell programming. This project aims to harness cutting-edge deep learning and single-cell omics technologies to improve the accuracy and efficiency of cell programming,

Description

Interpretable deep learning for cell programming. This project aims to harness cutting-edge deep learning and single-cell omics technologies to improve the accuracy and efficiency of cell programming, where one cell type is converted into another. This project expects to generate new knowledge of molecular networks and interdisciplinary approaches that utilise such knowledge for addressing key challenges in cell programming. Expected outcomes of this project include the development of advanced computational models that make cell programming more accurate, efficient, and reproducible. This should provide significant benefits by accelerating advancements in synthetic biology, and enhancing efficacy and efficiency in bioproduction and biomanufacturing.. Scheme: ARC Future Fellowships. Field: 3102 - Bioinformatics and Computational Biology. Lead: A/Prof Pengyi Yang