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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Machine learning based methods to generate conformational ensembles of disordered proteins (len18, point mutation)

Authors: Ishan Taneja;

Machine learning based methods to generate conformational ensembles of disordered proteins (len18, point mutation)

Abstract

data is organized by bin number (0-9) and mutation location (4, 8, 12). (Note: in the manuscript, we used the nomenclature bins 1-10 and mutation locations 5,8,13. We simply used a 0-index convention when naming our folders). All data is in rep_1, which contains the trajectory (xtc) file, Rg information (in the file Rg.out), pairwise distance information (in the file traj_analysis_data/pairwise_distance_matrix.csv) and the bspline coefficients (in the file bspline_info/xyz_coeff.npy). However, for bin_num=9/mutation_loc=12, the data used is in rep_2, not rep_1. 

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average