An Integrated Multi-Model Framework Utilizing Convolutional Neural Networks Coupled with Feature Extraction for
Muhammad Tahir1, Shahid Hussain2, Fawaz Khaled Alarfaj3
1Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T5V6, Canada; Department of Computer Science, Abdul Wali Khan University, Mardan, 23200, Pakistan.
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N4-methylcytosine (4mC) is a chemical modification that occurs on one of the four nucleotide bases in DNA and plays a vital role in DNA expression, repair, and replication. It also actively participates in the regulation of cell differentiation and gene expression. Consequently, it is important to comprehend the role of 4mC in the epigenetic regulation for revealing the complications of the gene expression and their associated governing cellular operations. However, the inherent resource requirements and time constraints of the experimental procedure, present challenges to the cellular culture process. While data-driven methodologies present promising solutions to mitigate the demand for extensive experimental efforts, their performance relies on the suitability and existence of high-quality data. This study presents a multi-model framework that integrates convolutional neural network (CNN) with the distributed k-mer and embedding feature extraction techniques to enhance the identification of 4mC sites in DNA sequences. The integration of k-mers ensures the effective representation of the local sequence patterns, while the utilization of embedding enables a more holistic encoding by considering the broader context and semantics of the sequence data. Following the initial step, the obtained distributed representation of the DNA sequence seamlessly enters the CNN, triggering a crucial convolution operation wherein a set of adaptable filters systematically convolves across the sequence to detect vital local patterns. The proposed integrated multi-model framework was applied to six publicly available datasets and evaluated against the cutting-edge 4mCPred, 4mCCNN, iDNA4mC, Meta-4mCpred, DeepTorrent, 4mCPred-SVM, and DMKL-HFIS methods. The evaluation was based on accuracy, specificity, sensitivity, and Matthews Correlation Coefficient. The results demonstrated that the proposed multi-model framework outperformed the state-of-the-art methods, as well as one-hot encoding and the hybrid of one-hot & TNC features, in accurately identifying 4mC sites.
