Horizon-wise Learning Paradigm Promotes Gene Splicing Identification
<|reference_start|>Horizon-wise Learning Paradigm Promotes Gene Splicing Identification: 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.<|reference_end|>
@article{li2024horizon-wise,
    title={Horizon-wise Learning Paradigm Promotes Gene Splicing Identification},
    author={Qi-Jie Li and Qian Sun and Shao-Qun Zhang},
    journal={arXiv preprint arXiv:2406.11900},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11900},
    primaryClass={q-bio.QM cs.AI cs.LG}
}