Virtual-cell models treat the whole perturbation response as the answer; Deep Genomics inverts the task, learning the roughly 1% direct effects and holding up on unseen transcripts
Deep Genomics subtracts the whole-transcriptome perturbation response that virtual-cell models usually try to predict, treating it as noise, and trains a sequence model, DeepRNAi, only on the direct effects between an siRNA and its target transcript. Direct effects account for about 1% of all siRNA-RNA interactions, and uncalibrated virtual-cell objectives had treated that signal as a rounding error. On a held-out-transcript split, DeepRNAi performs about the same as on seen transcripts, while a conventional machine-learning approach without a direct-effect focus loses roughly 50% of its above-baseline performance.