Horizon-wise Learning Paradigm Promotes Gene Splicing Identification

• URL: http://arxiv.org/abs/2406.11900v1
• Date: Sat, 15 Jun 2024 08:18:09 GMT
• Title: Horizon-wise Learning Paradigm Promotes Gene Splicing Identification
• Authors: Qi-Jie Li, Qian Sun, Shao-Qun Zhang,
• Abstract summary: 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.
• Score: 6.225959701339916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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