R-universe search: needs:MLInterfaces

Showing 6 of total 6 results (show query)

bioc

pRoloc:A unifying bioinformatics framework for spatial proteomics

The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation.

Maintained by Lisa Breckels. Last updated 5 days ago.

immunooncology proteomics massspectrometry classification clustering qualitycontrol bioconductor proteomics-data spatial-proteomics visualisation openblas cpp

15 stars 10.31 score 101 scripts 2 dependents

bioc

pRolocGUI:Interactive visualisation of spatial proteomics data

The package pRolocGUI comprises functions to interactively visualise spatial proteomics data on the basis of pRoloc, pRolocdata and shiny.

Maintained by Lisa Breckels. Last updated 5 months ago.

proteomics visualization gui

8 stars 6.90 score 3 scripts

bioc

bandle:An R package for the Bayesian analysis of differential subcellular localisation experiments

The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data.

Maintained by Oliver M. Crook. Last updated 2 months ago.

bayesian classification clustering immunooncology qualitycontrol dataimport proteomics massspectrometry openblas cpp openmp

4 stars 5.56 score 3 scripts

bioc

SigCheck:Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata

While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata.

Maintained by Rory Stark. Last updated 2 months ago.

geneexpression classification genesetenrichment

3.00 score 1 scripts

lcougnaud

nlcv:Nested Loop Cross Validation

Nested loop cross validation for classification purposes for misclassification error rate estimation. The package supports several methodologies for feature selection: random forest, Student t-test, limma, and provides an interface to the following classification methods in the 'MLInterfaces' package: linear, quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of the classifier are included: plot of the ranks of the features, scores plot for a specific classification algorithm and number of features, misclassification rate for the different number of features and classification algorithms tested and ROC plot. For further details about the methodology, please check: Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004) <doi:10.2202/1544-6115.1078>.

Maintained by Laure Cougnaud. Last updated 7 years ago.

2.00 score 8 scripts

cran

dGAselID:Genetic Algorithm with Incomplete Dominance for Feature Selection

Feature selection from high dimensional data using a diploid genetic algorithm with Incomplete Dominance for genotype to phenotype mapping and Random Assortment of chromosomes approach to recombination.

Maintained by Nicolae Teodor Melita. Last updated 8 years ago.

1 stars 1.00 score