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• W4387358414 abstract "Abstract The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigate the impact of data limitations and make significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirmed the significant improvements achieved by our models in predicting protein-protein binding affinity. The source codes for these three models are available at https://github.com/minghuilab/BindPPI ." @default.
• W4387358414 created "2023-10-06" @default.
• W4387358414 creator A5003705537 @default.
• W4387358414 creator A5029039847 @default.
• W4387358414 creator A5045677847 @default.
• W4387358414 creator A5051405817 @default.
• W4387358414 creator A5071684691 @default.
• W4387358414 date "2023-10-05" @default.
• W4387358414 modified "2023-10-18" @default.
• W4387358414 title "Systematic Investigation of Machine Learning on Limited Data: A Study on Predicting Protein-Protein Binding Strength" @default.
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• W4387358414 doi "https://doi.org/10.1101/2023.10.03.560786" @default.

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