Ensemble Based Machine Learning Approach for Predicting Methylation States
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
Show 2 earlier changesHide 2 earlier changes
- ERROR11 OK, 1 ERROR2026-04-25
- OK14 OK2026-03-10
Documentation
- Help pages
- 2
- Examples that run
- 0%2 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 100%
Downloads
Dependencies
Installing EpiSemble adds 194 packages (90 with compiled code, 128 MB of source tarballs). Heaviest: stringi 11 MB, TH.data 8.2 MB, ggplot2 6.0 MB. 1 more not on CRAN is not measured.
Code & Tests
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- unarchivedReturned to CRAN2023-06-04
- 0.1.1Latest
- RR 4.3.0 released · 2023-04-21
- archivedRemoved from CRAN2023-04-07issues were not corrected in time
- 0.1.02022-08-22
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-08-22
- Total releases
- 2 / 4 yrs
- License
- GPL-3 OSI
- Download size
- 1.0 MB
Cite
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