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School of Information Science and Technology, Northeast Normal University, Changchun 130117, China; Key Laboratory of Intelligent Information Processing of Jilin Province, Northeast Normal University, Changchun 130117, China; Institute of Computational Biology, Northeast Normal University, Changchun 130117, China. Electronic address:
Published: July 2022
AI Article Synopsis
N4-methylcytosine (4mC) is a DNA modification that plays a role in important biological processes like gene expression and transcription regulation.*
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This study developed a deep learning model using a 1-D CNN to accurately predict 4mC sites in the Escherichia coli genome by encoding DNA sequences with the word2vec technique.*
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The model achieved an accuracy of 86.1%, surpassing existing models by 4.3%, and the researchers made the data and source code available for public use on GitHub.*
Article Abstract
N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique 'word2vec'. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V.
