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RF-Fold specifications

Information


Unique identifier OMICS_10828
Name RF-Fold
Interface Web user interface
Restrictions to use None
Input data Plain protein sequence
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Jianlin Cheng

Publication for RF-Fold

RF-Fold citations

 (3)
library_books

A Metabolomic Signature of Acute Caloric Restriction

2017
PMCID: 5718701
PMID: 29029202
DOI: 10.1210/jc.2017-01020

[…] tricarboxylic acid (TCA) cycle (). As expected, CR was associated with a decrease in glucose (fold change 0.84, P = 5.9 × 10−5) and pyruvate (fold change 0.32, P = 9.0 × 10−4), which were restored by RF (fold change 1.14 and 10.8, P = 8.0 × 10−4 and 1.2 × 10−7, respectively) (). Citrate and aconitate were significantly elevated with CR, there was no change in isocitrate or α-ketoglutarate, but a s […]

library_books

In vivo probing of nascent RNA structures reveals principles of cotranscriptional folding

2017
Nucleic Acids Res
PMCID: 5766169
PMID: 28934475
DOI: 10.1093/nar/gkx617

[…] 50 -nw 50 -wo 25). The rf-norm tool generates a XML file for each transcript (or for each transcription intermediate/decile in the case of SPET-seq data). XML files for mature RNA were passed to the rf-fold tool of the RNA Framework (using ViennaRNA Package 2.2 with soft constraints ()) to infer mature RNA structures (parameters: -md 600 -nlp). […]

library_books

Adaptive local learning in sampling based motion planning for protein folding

2016
BMC Syst Biol
PMCID: 4977477
PMID: 27490494
DOI: 10.1186/s12918-016-0297-9

[…] ition problem is defined as a binary classification problem of predicting whether or not the unknown fold of the input protein is similar to an already known template from a protein structure library.RF-Fold uses random forests, a highly scalable classification method, to recognize protein folds []. A random forest is composed of many decision trees that are each trained on datasets of target-temp […]

Citations

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RF-Fold institution(s)
Department of Computer Science, Informatics Institute, C Bond Life Science Center, University of Missouri, Columbia, MO, USA
RF-Fold funding source(s)
The work was partially supported by an NIH grant (R01GM093123).

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