Search alternatives:
protein optimization » process optimization (Expand Search), property optimization (Expand Search), driven optimization (Expand Search)
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
based protein » capsid protein (Expand Search), based proteomics (Expand Search)
data derived » data driven (Expand Search)
lines based » lens based (Expand Search), genes based (Expand Search), lines used (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
protein optimization » process optimization (Expand Search), property optimization (Expand Search), driven optimization (Expand Search)
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
based protein » capsid protein (Expand Search), based proteomics (Expand Search)
data derived » data driven (Expand Search)
lines based » lens based (Expand Search), genes based (Expand Search), lines used (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
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<i>hi</i>PRS algorithm process flow.
Published 2023“…<b>(C)</b> The whole training data is then scanned, searching for these sequences and deriving a re-encoded dataset where interaction terms are binary features (i.e., 1 if sequence <i>i</i> is observed in <i>j</i>-th patient genotype, 0 otherwise). …”
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Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential
Published 2023“…In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). …”
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Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential
Published 2023“…In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). …”
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Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes
Published 2022“…Our estimate of m is the maximizer of a marginal likelihood obtained by integrating the latent log-ORs out of the joint distribution of the parameters and observed data. We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation. …”
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