首页|Study Findings on Machine Learning Are Outlined in Reports from Michigan State University (Integration of Persistent Laplacian and Pre-trained Transformer for Protein Solubility Changes Upon Mu- tation)
Study Findings on Machine Learning Are Outlined in Reports from Michigan State University (Integration of Persistent Laplacian and Pre-trained Transformer for Protein Solubility Changes Upon Mu- tation)
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Elsevier
Data detailed on Machine Learning have been presented. According to news reporting from East Lansing, Michigan, by NewsRx journalists, research stated, "Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR)." Financial supporters for this research include National Institutes of Health (NIH) - USA, National Science Foundation (NSF), National Aeronautics & Space Administration (NASA), MSU Foundation, Bristol-Myers Squibb, Pfizer, Nanyang Technological University, Ministry of Education, Singapore. The news correspondents obtained a quote from the research from Michigan State University, "Part of the challenge stems from the fact that there is no three-dimensional (3D) structures for the wild - type and mutant proteins. This work integrates persistent Laplacians and pre -trained Transformer for the task. The Transformer, pretrained with hundreds of millions of protein sequences, embeds wild -type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2. The resulting machine learning model was trained on an extensive data set labeled with three solubility types." According to the news reporters, the research concluded: "Our model outperforms all existing predictive methods and improves the state-of-the-art up to 15%."
East LansingMichiganUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesGeneticsMachine LearningMichigan State University
NETL
NSTL
Elsevier
