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/.

BACKGROUND: Antioxidant proteins are involved in several biological processes and can protect DNA and cells from the damage of free radicals. These proteins regulate the body's oxidative stress and perform a significant role in many antioxidant-based drugs. The current invitro-based medications are costly, time-consuming, and unable to efficiently screen and identify the targeted motif of antioxidant proteins. METHODS: In this model, we proposed an accurate prediction method to discriminate antioxidant proteins namely StackedEnC-AOP. The training sequences are formulation encoded via incorporating a discrete wavelet transform (DWT) into the evolutionary matrix to decompose the PSSM-based images via two levels of DWT to form a Pseudo position-specific scoring matrix (PsePSSM-DWT) based embedded vector. Additionally, the Evolutionary difference formula and composite physiochemical properties methods are also employed to collect the structural and sequential descriptors. Then the combined vector of sequential features, evolutionary descriptors, and physiochemical properties is produced to cover the flaws of individual encoding schemes. To reduce the computational cost of the combined features vector, the optimal features are chosen using Minimum redundancy and maximum relevance (mRMR). The optimal feature vector is trained using a stacking-based ensemble meta-model. RESULTS: Our developed StackedEnC-AOP method reported a prediction accuracy of 98.40% and an AUC of 0.99 via training sequences. To evaluate model validation, the StackedEnC-AOP training model using an independent set achieved an accuracy of 96.92% and an AUC of 0.98. CONCLUSION: Our proposed StackedEnC-AOP strategy performed significantly better than current computational models with a ~ 5% and ~ 3% improved accuracy via training and independent sets, respectively. The efficacy and consistency of our proposed StackedEnC-AOP make it a valuable tool for data scientists and can execute a key role in research academia and drug design.