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flowml  

A Backend for a 'nextflow' Pipeline that Performs Machine-Learning-Based Modeling of Biomedical Data
View on CRAN: Click here


Download and install flowml package within the R console
Install from CRAN:
install.packages("flowml")

Install from Github:
library("remotes")
install_github("cran/flowml")

Install by package version:
library("remotes")
install_version("flowml", "0.1.3")



Attach the package and use:
library("flowml")
Maintained by
Sebastian Malkusch
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-10-09
Latest Update: 2024-02-16
Description:
Provides functionality to perform machine-learning-based modeling in a computation pipeline. Its functions contain the basic steps of machine-learning-based knowledge discovery workflows, including model training and optimization, model evaluation, and model testing. To perform these tasks, the package builds heavily on existing machine-learning packages, such as 'caret' and associated packages. The package can train multiple models, optimize model hyperparameters by performing a grid search or a random search, and evaluates model performance by different metrics. Models can be validated either on a test data set, or in case of a small sample size by k-fold cross validation or repeated bootstrapping. It also allows for 0-Hypotheses generation by performing permutation experiments. Additionally, it offers methods of model interpretation and item categorization to identify the most informative features from a high dimensional data space. The functions of this package can easily be integrated into computation pipelines (e.g. 'nextflow' ) and hereby improve scalability, standardization, and re-producibility in the context of machine-learning.
How to cite:
Sebastian Malkusch (2023). flowml: A Backend for a 'nextflow' Pipeline that Performs Machine-Learning-Based Modeling of Biomedical Data. R package version 0.1.3, https://cran.r-project.org/web/packages/flowml
Previous versions and publish date:
0.1.2 (2023-10-09 14:50)
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