Abstract
In this study, we develop a deep learning framework aimed at predicting the impacts of chemical perturbations on individual cells, emphasizing the encoding of small molecular chemical structures . Utilizing the LINCS L1000 dataset, the approach incorporates transfer learning with the ChemBERTa model to navigate the challenges of high-dimensional single-cell data analysis and sparse dataset limitations. The framework integrates computational models such as Transformer, DenseNet, and CNN, designed to understand cellular responses to perturbations at the single-cell level. Validated through 5-fold cross-validation, the ensemble model combines the strengths of individual models to improve prediction accuracy and robustness for cellular responses to perturbations. This study proposes a novel encoding scheme for small molecule chemical structures and cell types, integrating various computational models to contribute to the development of predictive models for small molecule perturbations. It offers a step towards enhancing the understanding of complex biological responses through deep learning methodologies.
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Acknowledgments
The authors would like to express their sincere gratitude to Professor Xiaolin Hu for his invaluable guidance and support throughout the research. The authors extend their gratitude to the Huawei Intelligent Foundation for supplying the computational services. The work was supported by the National Natural Science Foundation of China under Grant U2341228. Furthermore, the authors appreciate the datasets provided by Kaggle, which were instrumental in completing this study.
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Zhang, C., Bi, F., Zhang, J., Chen, G. (2024). A Deep Learning Approach for Single-Cell Perturbation Prediction Using Small Molecule Chemical Structures. In: Le, X., Zhang, Z. (eds) Advances in Neural Networks – ISNN 2024. ISNN 2024. Lecture Notes in Computer Science, vol 14827. Springer, Singapore. https://doi.org/10.1007/978-981-97-4399-5_54
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DOI: https://doi.org/10.1007/978-981-97-4399-5_54
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