{ "_id": "6a1adb4a1d7bb097a09e8f0d", "Package": "EpiSemble", "Type": "Package", "Title": "Ensemble Based Machine Learning Approach for Predicting\nMethylation States", "Version": "0.1.1", "Authors@R": "c(person(\" Dipro\", \"Sinha\", role = c(\"aut\",\"cre\"), email = \"diprosinha@gmail.com\"),\nperson(\"Sunil\", \"Archak\", role = \"aut\", email = \"sunil.archak@gmail.com\"),\nperson(\"Dwijesh Chandra\", \"Mishra\", role = \"aut\", email = \"dwij.mishra@gmail.com\"),\nperson(\"Tanwy\", \"Dasmandal\", role = \"aut\", email = \"tanwydasmandal20@gmail.com\"),\nperson(\"Md\", \"Yeasin\", role = \"aut\", email = \"yeasin.iasri@gmail.com\"))", "Author": "Dipro Sinha [aut, cre], Sunil Archak [aut], Dwijesh Chandra\nMishra [aut], Tanwy Dasmandal [aut], Md Yeasin [aut]", "Maintainer": "Dipro Sinha ", "Description": "DNA methylation (6mA) is a major epigenetic process by\nwhich alteration in gene expression took place without changing\nthe DNA sequence. Predicting these sites in-vitro is laborious,\ntime consuming as well as costly. This 'EpiSemble' package is\nan in-silico pipeline for predicting DNA sequences containing\nthe 6mA sites. It uses an ensemble-based machine learning\napproach by combining Support Vector Machine (SVM), Random\nForest (RF) and Gradient Boosting approach to predict the\nsequences with 6mA sites in it. This package has been developed\nby using the concept of Chen et al. 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