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GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling (2021)
Journal Article
Louison, K. A., Louison, K. A., Dryden, I. L., & Laughton, C. A. (2021). GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling. Journal of Chemical Theory and Computation, 17(12), 7930-7937. https://doi.org/10.1021/acs.jctc.1c00735

We describe a general approach to transforming molecular models between different levels of resolution, based on machine learning methods. The approach uses a matched set of models at both levels of resolution for training, but requires only the coor... Read More about GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling.

Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip (2021)
Journal Article
Burroughs, L., Amer, M., Vassey, M., Koch, B., Figueredo, G., Mukonoweshuro, B., …Alexander, M. R. (2021). Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip. Biomaterials, 271, Article 120740. https://doi.org/10.1016/j.biomaterials.2021.120740

© 2021 The Authors Human mesenchymal stem cells (hMSCs) are widely represented in regenerative medicine clinical strategies due to their compatibility with autologous implantation. Effective bone regeneration involves crosstalk between macrophages an... Read More about Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip.

Non?parametric regression for networks (2021)
Journal Article
Severn, K. E., Dryden, I. L., & Preston, S. P. (2021). Non?parametric regression for networks. Stat, 10(1), Article e373. https://doi.org/10.1002/sta4.373

Network data are becoming increasingly available, and so there is a need to develop suitable methodology for statistical analysis. Networks can be represented as graph Laplacian matrices, which are a type of manifold-valued data. Our main objective i... Read More about Non?parametric regression for networks.