iPro-CSAF: identification of promoters based on convolutional spiking neural networks and spiking attention mechanism
Qian Zhou, Jie Meng, Hao Luo
TL;DR
This paper introduces iPro-CSAF, a new computational method using spiking neural networks to accurately identify DNA promoters across multiple species.
Contribution
The novel contribution is the development of iPro-CSAF, a convolutional spiking neural network with spiking attention mechanism for promoter recognition.
Findings
iPro-CSAF outperforms existing methods in promoter recognition with fewer network parameters.
The method achieves good performance on both prokaryotic and eukaryotic promoters from seven species.
iPro-CSAF demonstrates low complexity and strong generalization for promoter identification.
Abstract
A promoter is a DNA segment which plays a key role in regulating gene expression. Accurate identification of promoters is significant for understanding the regulatory mechanisms involved in gene expression and genetic disease treatment. Therefore, it is an urgent challenge to develop computational methods for identifying promoters. Most current methods were designed for promoter recognition on few species and required complex feature extraction methods in order to attain high recognition accuracy. Spiking neural networks have inherent recurrence and use spike-based sparse coding. Therefore, they have good property of processing spatio-temporal information and are well suited for learning sequence information. In this study, iPro-CSAF, a convolutional spiking neural network combined with spiking attention mechanism is designed for promoter recognition. The method extracts promoter…
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Taxonomy
TopicsMachine Learning in Bioinformatics · Genomics and Phylogenetic Studies · Advanced biosensing and bioanalysis techniques