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IPred specifications

Information


Unique identifier OMICS_07776
Name IPred
Alternative name Integrative gene Prediction
Software type Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Java, Python
License GNU Lesser General Public License version 3.0
Computer skills Advanced
Stability Stable
Requirements
Matplotlib, Numpy
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline Bernhard Renard

Publication for Integrative gene Prediction

IPred citations

 (19)
call_split

Methyl group assignment using pseudocontact shifts with PARAssign

2017
J Biomol NMR
PMCID: 5736784
PMID: 29181729
DOI: 10.1007/s10858-017-0136-3
call_split See protocol

[…] for minimal cost assignment (Kuhn ) is used to perform the assignment of the input PCS, using a scoring factor cost function to populate the assignment matrix: equation 2Scoringfactor=1Pc×∑i=1PcδPCS,ipred-δPCS,iexp2δPCS,ipred+δPCS,iexp2where Pc is the number of paramagnetic centers. PARAssign attributes a scoring function of 1 to assignments for which experimental and predicted PCS have opposite […]

library_books

A population pharmacokinetic model for individualised dosage regimens of vancomycin in Chinese neonates and young infants

2017
Oncotarget
PMCID: 5739632
PMID: 29285245
DOI: 10.18632/oncotarget.22114

[…] Goodness-of-fit was evaluated by using diagnostic scatter plots as follows: (a) observed (DV) versus population predicted concentrations (PRED); (b) DV versus individual predicted concentrations (IPRED); (c) conditional weighted residuals (CWRES) versus time (IVAR); (d) CWRES versus PRED; (e) quantile-quantile plot of the components of conditional weighted residuals. The stability of the final […]

call_split

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

2017
PLoS One
PMCID: 5389810
PMID: 28403159
DOI: 10.1371/journal.pone.0174785
call_split See protocol

[…] the classification errors calculated at each run gives the overall cross validation error. We performed ten repetitions of a 10-fold cross validation, using the ‘errorest’ function in the R package ‘ipred’ []. […]

library_books

Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics

2017
PMCID: 5321813
PMID: 27884052
DOI: 10.1002/psp4.12161

[…] be sufficiently reliable at high levels of shrinkage (see specific section for more details). For this reason, shrinkage should be evaluated and reported to provide information about relevance of the IPRED‐based evaluation tools. Currently, there is no consensus on the level of shrinkage that renders these individual metrics no longer reliable. A shrinkage value of 30% or 50%, if calculated from S […]

library_books

Exploratory Population PK Analysis of Dupilumab, a Fully Human Monoclonal Antibody Against IL‐4Rα, in Atopic Dermatitis Patients and Normal Volunteers

2016
PMCID: 5655850
PMID: 27778477
DOI: 10.1002/psp4.12136

[…] of the individual data for a very small portion of observations due to the singularity in the concentration–time slope described above. Thus, the clustered observations observed in the log–log DV vs. IPRED figure were left in the analysis for the following reasons: 1) the appearance of a cluster in the log–log DV vs. individual PRED figure was an insufficient justification to remove observations; […]

library_books

Predicting non small cell lung cancer prognosis by fully automated microscopic pathology image features

2016
Nat Commun
PMCID: 4990706
PMID: 27527408
DOI: 10.1038/ncomms12474

[…] rees and Breiman's random forest were used to conduct supervised machine-learning. Models were built and tested using R version 3.2, with ‘e1071' package for SVM and naive Bayes classifiers, package ‘ipred' for bagging, package ‘randomforest' for Breiman's random forest, and package ‘party' for random forest with conditional inference trees. The data sets were randomly partitioned into 70% trainin […]

Citations

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IPred institution(s)
Research Group Bioinformatics (NG4), Robert Koch-Institute, Berlin, Germany

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