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Updated: Jun 5, 2025
High Sensitivity 5-hydroxymethylcytosine Detection in Balb/C Brain Tissue
Published on: February 1, 2011
m5c-iEnsem: 5-methylcytosine sites identification through ensemble models
Anas Bilal1,2, Fawaz Khaled Alarfaj3, Rafaqat Alam Khan4
1College of Information Science and Technology, Hainan Normal University, Haikou 571158, China.
Motivation:
5-Methylcytosine (m5c), a modified cytosine base, arises from adding a methyl group at the 5th carbon position. This modification is a prevalent form of post-transcriptional modification (PTM) found in various types of RNA. Traditional laboratory techniques often fail to provide rapid and accurate identification of m5c sites. However, with the growing accessibility of sequence data, expanding computational models offers a more efficient and reliable approach to m5c site detection. This research focused on creating advanced in-silico methods using ensemble learning techniques. The encoded data was processed through ensemble models, including bagging and boosting techniques. These models were then rigorously evaluated through independent testing and 10-fold cross-validation.
Results:
Among the models tested, the Bagging ensemble-based predictor, m5C-iEnsem, demonstrated superior performance to existing m5c prediction tools.
Availability And Implementation:
To further support the research community, m5c-iEnsem has been made available via a user-friendly web server at https://m5c-iensem.streamlit.app/.
