首页|AttentionSplice:An Interpretable Multi-Head Self-Attention Based Hybrid Deep Learning Model in Splice Site Prediction
AttentionSplice:An Interpretable Multi-Head Self-Attention Based Hybrid Deep Learning Model in Splice Site Prediction
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
Pre-mRNA splicing is an essential pro-cedure for gene transcription.Through the cutting of in-trons and exons,the DNA sequence can be decoded into different proteins related to different biological functions.The cutting boundaries are defined by the donor and ac-ceptor splice sites.Characterizing the nucleotides pat-terns in detecting splice sites is sophisticated and chal-lenges the conventional methods.Recently,the deep learning frame has been introduced in predicting splice sites and exhibits high performance.It extracts high di-mension features from the DNA sequence automatically rather than infers the splice sites with prior knowledge of the relationships,dependencies,and characteristics of nucleotides in the DNA sequence.This paper proposes the AttentionSplice model,a hybrid construction com-bined with multi-head self-attention,convolutional neural network,bidirectional long short-term memory network.The performance of AttentionSplice is evaluated on the Homo sapiens(Human)and Caenorhabditis Elegans(Worm)datasets.Our model outperforms state-of-the-art models in the classification of splice sites.To provide in-terpretability of AttentionSplice models,we extract im-portant positions and key motifs which could be essential for splice site detection through the attention learned by the model.Our result could offer novel insights into the underlying biological roles and molecular mechanisms of gene expression.
万方数据
