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khliland
multiblock:Multiblock Data Fusion in Statistics and Machine Learning
Functions and datasets to support Smilde, Næs and Liland (2021, ISBN: 978-1-119-60096-1) "Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences". This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting functions, several real data sets and six vignettes covering a range different applications.
Maintained by Kristian Hovde Liland. Last updated 13 hours ago.
16 stars 6.56 score 19 scripts
bioc
DaMiRseq:Data Mining for RNA-seq data: normalization, feature selection and classification
The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot.
Maintained by Mattia Chiesa. Last updated 5 months ago.
sequencingrnaseqclassificationimmunooncologyopenjdk
5.32 score 7 scripts 1 dependentsbioc
GARS:GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets
Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets.
Maintained by Mattia Chiesa. Last updated 5 months ago.
classificationfeatureextractionclusteringopenjdk
5.00 score 2 scriptskhliland
ER:Effect + Residual Modelling
Multivariate modeling of data after deflation of interfering effects. EF Mosleth et al. (2021) <doi:10.1038/s41598-021-82388-w> and EF Mosleth et al. (2020) <doi:10.1016/B978-0-12-409547-2.14882-6>.
Maintained by Kristian Hovde Liland. Last updated 2 years ago.
3.00 score 1 scripts