iPro-TCN: Prediction of DNA Promoters Recognition and Their Strength Using Temporal Convolutional Network
Ali Raza, Waleed Alam, Shahzad Khan, Muhammad Tahir, Kil To Chong
Abstract
Promoters are important regulatory elements in the genome that control gene expression, and their abnormalities have been linked to various diseases. With the development of high-throughput sequencing techniques, there is a need for computational methods to identify promoters in large amounts of DNA sequence data. However, many existing methods rely on a single feature representation approach, which can potentially lead to information loss. In order to address this issue. We proposed a computational model iPro-TCN that combines Temporal Convolutional Network (TCN) with a word2vec feature representation. This model includes a new feature descriptor called K-mer word vector and has been shown to have high sensitivity and accuracy in distinguishing promoters, including strong and weak promoters. The iPro-TCN model was evaluated on a benchmark dataset and was able to achieve good performance in both promoter identification and promoter strength prediction. Specifically, the model had an accuracy of 91.86% in the first layer for promoter identification, and an accuracy of 84.63% in the second layer for promoter strength prediction. These results suggest that the iPro-TCN model is a strong performer in predicting promoter sequences and promoter strength in DNA. These results are far higher than the existing best predictor, which indicates that our model showcases a better performance compared with the existing approaches.