A collection of python scripts used to generate CML from csv (CSV_to_CML.py), apply machine learning (Gradient Boosting using decision trees, prediction_viscosity.py), model based on eq 6 in the associated publication, and to plot the generated data (plot*.py).
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Created: 23rd Aug 2019 at 09:19
Last updated: 16th Jun 2020 at 07:51
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Version 4 (latest) Created 16th Jun 2020 at 07:51 by Xinmeng Xu
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Projects: Simulation Foundries, CML for thermophysical properties of mixtures, Test Project (dummy), Towards Reproducible Enzyme Modeling
Institutions: University of Stuttgart, Karlsruhe Institute of Technology (KIT)
https://orcid.org/0000-0001-9119-1778Expertise: enzyme kinetics, enzymes, Enzymatic reactions, biochemical enzyme characterization, Biochemistry, molecular simulation, molecular modeling, Programming, Bioinformatics, Computational Biology
Tools: Gromacs, Python, Molecular Dynamics, bash, Biochemistry, Bioinformatics, Biochemistry and protein analysis, Enzyme assay, enzyme kinetics, isothermal titration calorimetry, dynamic light scattering, Spectrophotometry
Polyglot European Scientist. I thrive working in interdisciplinary environments combining the study of enzyme reactions and mechanisms with bioinformatics, molecular modelling, automated data analysis and data stewardship.
This is a collection of deep eutectic solvent (DES) experimental and simulation data that is stored in CML format and analysed using gradient boosting decision trees.
Submitter: Gudrun Gygli
Studies: Choline chloride:glycerol:water mixtures in CML, Meta-analysis of viscosity of aqueous deep eutectic solvents and their c...
Assays: CML processing and analysis, VFT and Arrhenius Modelling
Snapshots: No snapshots
This is a collection of data involving choline chloride:glycerol:water mixtures stored in CML.
Snapshots: Snapshot 1, Snapshot 2, Snapshot 3, Snapshot 4
This is a collection of data that have been used to analyse data on deep eutectic solvent mixtures of choline chloride:glycerol:water.
Submitter: Gudrun Gygli
Biological problem addressed: Model Analysis Type
Investigation: Deep Eutectic Solvents
Organisms: No organisms
Models: No Models
SOPs: No SOPs
Data files: CML files, CSV data, Python scripts for CML
Snapshots: No snapshots
Abstract
Authors: Xinmeng Xu, Jan Range, Gudrun Gygli, Jürgen Pleiss
Date Published: 12th Mar 2020
Publication Type: Journal
Citation: J. Chem. Eng. Data 65(3):1172-1179