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# RNAmodR.ML [![Build Status](]( [![codecov](]( Post-transcriptional modifications can be found abundantly in rRNA and tRNA and can be detected classically via several strategies. However, difficulties arise if the identity and the position of the modified nucleotides is to be determined at the same time. Classically, a primer extension, a form of reverse transcription (RT), would allow certain modifications to be accessed by blocks during the RT changes or changes in the cDNA sequences. Other modification would need to be selectively treated by chemical reactions to influence the outcome of the reverse transcription. With the increased availability of high throughput sequencing, these classical methods were adapted to high throughput methods allowing more RNA molecules to be accessed at the same time. However, patterns of some modification cannot be detected by accessing small number of parameters. For these cases machine learning models can be trained on data from positions known to be modified in order to detect additional modified positions. `RNAmodR.ML` implements additional classes from the base package `RNAmodR` to train and use machine learning models. The package contains an example workflow for random forest models with the `ranger` package ([Wright & Ziegler 2017](#Literature)). In addition classes for using deep learning models generated with the `keras` package are also implemented ([Allaire & Chollet 2018](#Literature)). Classes for other machine learning models can also be easily implemented. # Installation The current version of the RNAmodR.Data package is available from Bioconductor. ``` if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("RNAmodR.ML") library(RNAmodR.ML) ``` # Introduction The `ModifierML` class extends the `Modifier` class from the `RNAmodR` package and adds one slot, `mlModel`, a getter/setter `getMLModel`/`setMLModel`, an additional `useMLModel` function. For different types of models `ModifierMLModel` derived classes are available, which currently are: * `ModifierMLranger` for models generated with the `ranger` package ([Wright & Ziegler 2017](#Literature)) * `ModifierMLkeras` for models generated with the `keras` package ([Allaire & Chollet 2018](#Literature)) An trained model can be used to create a `ModifierMLModel` object. The generated `ModifierMLModel` object can then be set for the `ModifierML` object using the `setMLModel` function. For more details, please have a look at the vignette. # Literature - Marvin N. Wright and Andreas Ziegler (2017): "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R". Journal of Statistical Software 77 (1): 1-17. - JJ Allaire and Fran├žois Chollet (2018): "keras: R Interface to 'Keras'". R package version 2.2.4 (