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New kernels boost protein property prediction over foundation models

By PulseAugur Editorial · [2 sources] ·

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.

RANK_REASON The cluster contains an academic paper detailing a new method for protein property prediction.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Gevorg Grigoryan ·

    Flexible Kernels for Protein Property Prediction

    Despite its importance to applications in protein design, predicting protein properties like binding affinity and thermostability from sparse experimental data remains a significant challenge. Accordingly, we introduce a class of sequence kernels that exploit evolutionary substit…

  2. arXiv stat.ML TIER_1 English(EN) · Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani, Henry N. Ward, Hunter Nisonoff, James M. McFarland, Gevorg Grigoryan ·

    Flexible Kernels for Protein Property Prediction

    arXiv:2606.11057v1 Announce Type: cross Abstract: Despite its importance to applications in protein design, predicting protein properties like binding affinity and thermostability from sparse experimental data remains a significant challenge. Accordingly, we introduce a class of …

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