demystifying
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delemottelab
The [link](https://drive.google.com/drive/folders/19V1mXz7Yu0V_2JZQ8wtgt7aZusAKs2Bb?usp=sharing) to the input data specified in the README appears to be broken and leads to a 404 error. It is also broken in the published document.
Hi, I have done the setup suggested in the Readme file for importing **demystifying**, then I moved to the folder **bpj_paper_input** and tried the command: `python run_benchmarks.py --extractor_type PCA` this...
Works better in dev/prod environment IMO and its easier to package.
Given some feature type supported by GROMACS (dihedrals, distances etc) and a topology of the system, modify the index file and generate the pull code MDP input for enhanced sampling...
McInnes et al. suggest using UMAP can speed up some calculations, and is more interpretable than t-SNE. https://pypi.org/project/umap-learn/
See https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html We can probably use the coef_ attribute directly as importance per feautre.
For protein with many chains, several chains may have the same residue ID. We should support an optional third column to feature_to_resids to specify the chain index. This should also...