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Found 4 papers, 3 papers with code
Individual health-disease phase diagrams for disease prevention based on machine learning
Here, we present the health-disease phase diagram (HDPD), which represents a personal health state by visualizing the boundary values of multiple biomarkers that fluctuate early in the disease progression process.
Efficient Construction Method for Phase Diagrams Using Uncertainty Sampling
Furthermore, we show that using the US approach, undetected new phase can be rapidly found, and smaller number of initial sampling points are sufficient.
Materials Science Computational Physics
Population-based de novo molecule generation, using grammatical evolution
Automatic design with machine learning and molecular simulations has shown a remarkable ability to generate new and promising drug candidates.
Chemical Physics Biomolecules
ChemTS: An Efficient Python Library for de novo Molecular Generation
Automatic design of organic materials requires black-box optimization in a vast chemical space.
Chemical Physics Computational Engineering, Finance, and Science
