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Sequence-structure based prediction of pathogenicity for amino acid substitutions in proteins associated with primary immunodeficiencies
Published 2025-02-01“…The best SSPR models demonstrated high accuracy, with an average ROC AUC of 0.831 ± 0.037, a Balanced accuracy of (0.763 ± 0.034), MCC (0.457 ± 0.06), and F-measure (0.623 ± 0.07) across all genes, outperforming the most popular bioinformatics tools.ConclusionsThe best created SSPR models for the prediction of pathogenicity of amino acid substitutions related with PIDs have been implemented in a freely available web application SAV-Pred (Single Amino acid Variants Predictor, http://www.way2drug.com/SAV-Pred/), which may be a useful tool for medical geneticists and clinicians. …”
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The autonomic response following taVNS predicts changes in level of consciousness in DoC patients
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Machine learning-based prediction of CO2 solubility in methyldiethanolamine solutions: A comparative study
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Structural pre-training improves physical accuracy of antibody structure prediction using deep learning.
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Predicting nighttime black ice using atmospheric data for efficient winter road maintenance patrols
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Robustness of machine learning predictions for Fe-Co-Ni alloys prepared by various synthesis methods
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Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method
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Predicting Psychological Well-being Based on Family Emotional Atmosphere: Mediating Role of Social Support
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