Flexible Kernels for Protein Property Prediction
Researchers have developed a new class of sequence kernels for Gaussian processes that improve protein property prediction. These kernels leverage evolutionary substitution matrices and local linearity, demonstrating superior data efficiency compared to methods relying on foundation model embeddings. The approach can also incorporate structural information from foundation models, making it suitable for multi-task learning across various protein property landscapes. AI
IMPACT Offers a more data-efficient alternative to foundation model embeddings for specific biological property predictions.