Horizon-wise Learning Paradigm Promotes Gene Splicing Identification

TL;DR

This paper introduces H-GSI, a novel horizon-wise learning framework for gene splicing identification that predicts all sequence positions simultaneously, significantly improving accuracy and efficiency over existing methods like SpliceAI.

Contribution

The paper proposes a new horizon-wise paradigm and a comprehensive framework for gene splicing detection, outperforming previous models in accuracy and computational efficiency.

Findings

H-GSI achieves 97.20% accuracy on real-world data.

H-GSI outperforms SpliceAI in accuracy and efficiency.

The horizon-wise approach enables simultaneous prediction of all sequence positions.

Abstract

Identifying gene splicing is a core and significant task confronted in modern collaboration between artificial intelligence and bioinformatics. Past decades have witnessed great efforts on this concern, such as the bio-plausible splicing pattern AT-CG and the famous SpliceAI. In this paper, we propose a novel framework for the task of gene splicing identification, named Horizon-wise Gene Splicing Identification (H-GSI). The proposed H-GSI follows the horizon-wise identification paradigm and comprises four components: the pre-processing procedure transforming string data into tensors, the sliding window technique handling long sequences, the SeqLab model, and the predictor. In contrast to existing studies that process gene information with a truncated fixed-length sequence, H-GSI employs a horizon-wise identification paradigm in which all positions in a sequence are predicted with only one forward computation, improving accuracy and efficiency. The experiments conducted on the real-world Human dataset show that our proposed H-GSI outperforms SpliceAI and achieves the best accuracy of 97.20\%. The source code is available from this link.