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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >EDeepSSP: Explainable deep neural networks for exact splice sites prediction
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EDeepSSP: Explainable deep neural networks for exact splice sites prediction

机译：edeepssp：可解释用于精确的剪接网站预测的深度神经网络

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摘要

Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performance, their interpretability remains challenging. Moreover, all traditional machine learning methods manually extract features, which is tedious job. To address these challenges, we propose a deep learning-based approach (EDeepSSP) that employs convolutional neural networks (CNNs) architecture for automatic feature extraction and effectively predicts splice sites. Our model, EDeepSSP, divulges the opaque nature of CNN by extracting significant motifs and explains why these motifs are vital for predicting splice sites. In this study, experiments have been conducted on six benchmark acceptors and donor datasets of humans, cress, and fly. The results show that EDeepSSP has outperformed many state-of-the-art approaches. EDeepSSP achieves the highest area under the receiver operating characteristic curve (AUC_ROC) and area under the precision-recall curve (AUC_PR) of 99.32% and 99.26% on human donor datasets, respectively. We also analyze various filter activities, feature activations, and extracted significant motifs responsible for the splice site prediction. Further, we validate the learned motifs of our model against known motifs of JASPAR splice site database.
机译：接头位点预测对于了解基因调节，基因函数对于更好的基因组注释来说至关重要。存在许多计算方法，用于识别拼接站点。虽然大多数方法实现了绩效的绩效，但他们的可解释性仍然具有挑战性。此外，所有传统机器学习方法都会手动提取特征，这是繁琐的工作。为解决这些挑战，我们提出了一种基于深度学习的方法（DEDSEP），该方法采用卷积神经网络（CNNS）架构进行自动特征提取，有效地预测接头位点。我们的模型，edeepssp通过提取显着的主题来筛选CNN的不透明性质，并解释了为什么这些图案对于预测接头位点至关重要。在这项研究中，已经在六个基准接受者和捐助者数据集和捐赠者数据集的实验中进行了人类，水芹和飞行。结果表明，edeepssp已经超越了许多最先进的方法。 edeepssp分别实现了接收器下的最高面积在接收器下的最高面积，并且分别在人类捐赠者数据集中的99.32％和99.26％的99.32％和99.26％的区域下的区域。我们还分析了各种过滤活动，功能激活，并提取负责拼接站点预测的重要主题。此外，我们验证了我们模型的学习图案，针对jaspar剪接站点数据库的已知主题。

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