Sigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest
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Summary
To predict the variability of the spatial restraints in template-based modeling, a prediction model is devised, Sigma-RF, by using the random forest (RF) algorithm, and the average alignment quality of residues located between and at two aligned residues, quasi-local information, is the most contributing factor.
- Type
- article
- Published
- 2015-03-21
- Cited by
- 15
- References
- 42
- Access
- Open access
- OpenAlex
- https://openalex.org/W25886990
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4911423
Keywords
Rolling resistance, Truck, Automotive engineering, Fuel efficiency, Mechanism (biology)
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- The Atomistic Mechanism of Conformational Transition of Adenylate Kinase Investigated by Lorentzian Structure-Based Potential.
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- Use of Restraints from Consensus Fragments of Multiple Server Models To Enhance Protein-Structure Prediction Capability of the UNRES Force Field
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- MLACP: machine-learning-based prediction of anticancer peptides
- Ergodicity and model quality in template‐restrained canonical and temperature/Hamiltonian replica exchange coarse‐grained molecular dynamics simulations of proteins
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- PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions
- Exploring the Folding Mechanism of Small Proteins GB1 and LB1.
- Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling
- Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms
- BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors