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bioRxiv · 10.1101/2024.12.06.627290

Scouter: Predicting Transcriptional Responses to Genetic Perturbations with LLM embeddings

Zhu, O.·Li, J.
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Abstract

This paper addresses the challenging problem of predicting transcriptional outcomes-- the expression levels of all genes--in gene perturbation experiments and introduces a novel method called Scouter. By leveraging the capabilities of large language models and employing a neural network that facilitates easy training, Scouter overcomes key limitations of current approaches and accurately predicts the outcomes of single-gene and two-gene perturbations, reducing the error of state-of-the-art methods by half or more.

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Zhu, O., Li, J.. 2024-12-10. Scouter: Predicting Transcriptional Responses to Genetic Perturbations with LLM embeddings. https://doi.org/10.1101/2024.12.06.627290

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