  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.
