AI Tool scNET Deciphers Cellular Response to Cancer Therapies

Tel Aviv University researchers have introduced scNET [Single-cell Network-based Expression Technology], an AI-driven technique, to analyze cellular responses to treatments, particularly in cancer therapy. This system integrates single-cell sequencing data with gene interaction networks, clarifying gene interactions and cellular behaviors under therapeutic interventions. Cancer treatment is challenging due to tumor heterogeneity. scNET enhances single-cell RNA sequencing by accurately depicting cell populations and their behaviors. It minimizes noise in high-resolution data, identifying genetic changes influencing therapeutic responses. Ron Sheinin highlights scNET's ability to reveal how T cells change behavior in response to treatment, overcoming limitations of standard data. Prof. Asaf Madi suggests scNET could enhance therapeutic strategies by identifying treatment effects on immune responses. Prof. Roded Sharan emphasizes AI's role in deciphering biological data, aiming to develop innovative treatments. Published in Nature Methods, the research highlights the integration of AI with biomedicine, paving the way for personalized medical therapies.