imAIgenelab - Dynamic Cancer Insight with AI

Advances in microscopy now enable the generation of complex, high-content imaging data that can be interpreted as a form of functional and phenotypic omics. However, their systematic exploitation is often limited by the lack of appropriate analytical tools. At imAIgene-lab, we develop and apply computational science approaches, including computer vision, data analysis, and multi-omics integration with single-cell technologies, to transform dynamic imaging data into quantitative biological information. Our goal is to integrate functional phenotypes with molecular profiles to uncover biologically meaningful patterns relevant for drug response prediction, the design of combinatorial strategies in immuno-oncology, and the development of AI-driven personalized diagnostic and therapeutic approaches.