AI Target ID from Single Cell Transcriptomes

This figure illustrates a pipeline for identifying therapeutic targets using single-cell transcriptomics and a generative pre-trained transformer. Starting from whole human single-cell data, the model embeds and trains on gene expression patterns, enabling fine-tuned discrimination between healthy and disease states. The approach leverages the comprehensive scAtlas dataset and advanced AI techniques to uncover gene-disease relationships. This template is adapted from Dr. Jeremy Jenkins' keynote presentation from BioRender's virtual event 'Mastering the AI Era: Exclusive Insights from Novartis, MIT & Top Biotech Leaders'. KO=Knockout; KI= Knock-in.

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