Exploring the Transcriptional and Translational Features Using Deep Neural Networks for mRNAs Classification | IEEE Conference Publication | IEEE Xplore

Exploring the Transcriptional and Translational Features Using Deep Neural Networks for mRNAs Classification


Abstract:

Recent advent of the second and third generation of sequencing has uncovered many novel transcripts. These novel transcripts could have crucial functions in different bio...Show More

Abstract:

Recent advent of the second and third generation of sequencing has uncovered many novel transcripts. These novel transcripts could have crucial functions in different biological processes and might be related to challenging diseases and pathogenesis. However, whether these genes should be classified as protein coding RNAs (pcRNAs) or long non-coding RNAs (lncRNAs) is still debated and unclear. In this study we propose a coding potential classification framework based on deep neural networks and novel features from RNA-seq and Ribo-seq data to classify RNAs transcripts into protein coding and long non coding. As far as we know, this is the first method that uses RNA-seq and Ribo-seq as predictors to classify RNAs using a deep neural network model. Compared to other methods, the prediction of our method reached 97.4% accuracy.
Date of Conference: 21-23 April 2023
Date Added to IEEE Xplore: 15 September 2023
ISBN Information:
Conference Location: Hangzhou, China

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