Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction

• URL: http://arxiv.org/abs/2504.14361v1
• Date: Sat, 19 Apr 2025 17:35:54 GMT
• Title: Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction
• Authors: Till Rossner, Ziteng Li, Jonas Balke, Nikoo Salehfard, Tom Seifert, Ming Tang,
• Abstract summary: We propose an innovative methodology for predicting Cancer Drug Response (CDR) through the integration of the scGPT foundation model within the DeepCDR model.<n>Our approach utilizes scGPT to generate embeddings from gene expression data, which are then used as gene expression input data for DeepCDR.
• Score: 3.544038341047844
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this study, we propose an innovative methodology for predicting Cancer Drug Response (CDR) through the integration of the scGPT foundation model within the DeepCDR model. Our approach utilizes scGPT to generate embeddings from gene expression data, which are then used as gene expression input data for DeepCDR. The experimental findings demonstrate the efficacy of this scGPT-based method in outperforming previous related works, including the original DeepCDR model and the scFoundation-based model. This study highlights the potential of scGPT embeddings to enhance the accuracy of CDR predictions and offers a promising alternative to existing approaches.

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