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.

通过提出一种名为 Horizon-wise Gene Splicing Identification（H-GSI）的基于视野的基因剪接识别框架，本研究在现代人工智能和生物信息学的合作中解决了基因剪接的核心任务。与既有研究相比，H-GSI 应用了基于视野的识别模式，通过一次正向计算预测序列中的所有位置，提高了准确性和效率，在真实世界的人类数据集上表现优于 SpliceAI，达到了最佳准确率 97.20%。