Package: EpiSemble 0.1.1

EpiSemble: Ensemble Based Machine Learning Approach for Predicting Methylation States

DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This 'EpiSemble' package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) <doi:10.1093/bioinformatics/btz015>.

Authors:Dipro Sinha [aut, cre], Sunil Archak [aut], Dwijesh Chandra Mishra [aut], Tanwy Dasmandal [aut], Md Yeasin [aut]

EpiSemble_0.1.1.tar.gz
EpiSemble_0.1.1.zip(r-4.5)EpiSemble_0.1.1.zip(r-4.4)EpiSemble_0.1.1.zip(r-4.3)
EpiSemble_0.1.1.tgz(r-4.5-any)EpiSemble_0.1.1.tgz(r-4.4-any)EpiSemble_0.1.1.tgz(r-4.3-any)
EpiSemble_0.1.1.tar.gz(r-4.5-noble)EpiSemble_0.1.1.tar.gz(r-4.4-noble)
EpiSemble_0.1.1.tgz(r-4.4-emscripten)EpiSemble_0.1.1.tgz(r-4.3-emscripten)
EpiSemble.pdf |EpiSemble.html
EpiSemble/json (API)

# Install 'EpiSemble' in R:
install.packages('EpiSemble', repos = c('https://diprosinha.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 1 stars 5 scripts 234 downloads 2 exports 212 dependencies

Last updated 2 years agofrom:66778bf712. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 08 2025
R-4.5-winOKMar 08 2025
R-4.5-macOKMar 08 2025
R-4.5-linuxOKMar 08 2025
R-4.4-winOKMar 08 2025
R-4.4-macOKMar 08 2025
R-4.4-linuxOKMar 08 2025
R-4.3-winOKMar 08 2025
R-4.3-macOKMar 08 2025

Exports:epiPredImpFeatures

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