Aygul Minnegalieva

• Ursu, Eugen; Minnegalieva, Aygul; Rawat, Puneet; Chernigovskaya, Maria; Tacutu, Robi & Sandve, Geir Kjetil Ferkingstad [Show all 8 contributors for this article] (2025). Training data composition determines machine learning generalization and biological rule discovery. Nature Machine Intelligence. 7(8), p. 1206–1219. doi: 10.1038/s42256-025-01089-5. Full text in Research Archive Show summary

  Supervised machine learning models depend on training datasets containing positive and negative examples: dataset composition directly impacts model performance and bias. Given the importance of machine learning for immunotherapeutic design, we examined how different negative class definitions affect model generalization and rule discovery for antibody–antigen binding. Using synthetic-structure-based binding data, we evaluated models trained with various definitions of negative sets. Our findings reveal that high out-of-distribution performance can be achieved when the negative dataset contains more similar samples to the positive dataset, despite lower in-distribution performance. Furthermore, by leveraging ground-truth information, we show that binding rules associated with positive data change based on the negative data used. Validation on experimental data supported simulation-based observations. This work underscores the role of dataset composition in creating robust, generalizable and biology-aware sequence-based ML models

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