为提高瓦斯涌出量预测精度,降低煤矿回采工作面瓦斯涌出超限事故的风险,针对瓦斯涌影响因素众多、难以预测等问题,采用灰狼优化算法(Grey Wolf Optimization,GWO)双向长短期记忆神经网络(Bi-directional Long Short-Term Memory,BiLSTM...为提高瓦斯涌出量预测精度,降低煤矿回采工作面瓦斯涌出超限事故的风险,针对瓦斯涌影响因素众多、难以预测等问题,采用灰狼优化算法(Grey Wolf Optimization,GWO)双向长短期记忆神经网络(Bi-directional Long Short-Term Memory,BiLSTM)的组合模型预测瓦斯涌出量。首先,运用主成分分析法(Principal Components Analysis,PCA)处理瓦斯涌出影响因素,降低数据维度,以减少模型计算时的负担;其次,利用GWO优化BiLSTM模型的学习率(best_lr)、隐藏层层数(best_hd)以及正则化系数(best_l2),可有效避免局部最优解问题,并采用决定系数(R-Square,R^(2))、均方根误差(Root Mean Square Error,RMSE)和平均绝对误差(Mean Absolute Error,MAE)对所建模型预测的结果进行综合评价分析;最后,将该模型应用于内蒙古自治区某矿回采工作面预测瓦斯涌出量。结果显示:PCA GWO BiLSTM组合模型相比于长短期记忆神经网络(Long Short-Term Memory,LSTM)和双向长短期记忆神经网络对应的单一模型,其MAE分别降低20.81%、30.17%,RMSE分别降低0.063、0.142,R^(2)则分别提高了0.023、0.075,表明该模型在复杂因素条件下具有更高的精准度、泛化性和鲁棒性。展开更多
DNA N6-甲基腺嘌呤(6mA)是一种重要的表观遗传修饰,参与基因调控、DNA复制和修复等生物过程,对疾病研究也具有重要意义,准确识别DNA 6mA位点对理解其功能和机制至关重要。尽管现有的NA 6mA位点预测方法已取得较大成功,但在预测精度和跨...DNA N6-甲基腺嘌呤(6mA)是一种重要的表观遗传修饰,参与基因调控、DNA复制和修复等生物过程,对疾病研究也具有重要意义,准确识别DNA 6mA位点对理解其功能和机制至关重要。尽管现有的NA 6mA位点预测方法已取得较大成功,但在预测精度和跨物种泛化能力上仍有改进空间。本文提出了一种结合双向长短期记忆网络(BiLSTM)和卷积神经网络(CNN)的混合深度学习模型(BiLSTM→CNN)来提高对DNA 6mA位点预测的能力。模型首先采用one-hot、EIIP和DNA二聚体三种编码方式对DNA序列进行编码,然后在不同网络结构、层数和优化器下优化模型。通过在蔷薇科植物、水稻和拟南芥的数据集上的广泛实验表明,BiLSTM→CNN模型在蔷薇科植物中的准确率(ACC)为94.5%,在水稻中为93.8%,在拟南芥中为86.6%。与其他方法相比,BiLSTM→CNN模型在三个植物物种的6mA位点预测中均展现出良好的性能,并具有出色的跨物种泛化能力。DNA N6-methyladenine (6mA) is an important epigenetic modification involved in biological processes such as gene regulation, DNA replication, and repair, making it significant for disease research. Therefore, accurately identifying DNA 6mA sites is crucial for understanding their functions and mechanisms. Despite notable successes with existing methods, there is still room for improvement in prediction accuracy and cross-species generalization. In this study, we propose a hybrid deep learning model (BiLSTM→CNN) that integrates bidirectional long short-term memory networks (BiLSTM) and convolutional neural networks (CNN). Firstly, the model-encoded DNA sequences employ one-hot encoding, EIIP encoding, and DNA dimer encoding. And then optimized under various network architectures, layer configurations and optimizers. We conducted experiments on datasets from Rosaceae, rice and Arabidopsis thaliana, the results indicate that the BiLSTM→CNNmodel achieves an accuracy (ACC) of 94.5% for Rosaceae, 93.8% for rice, and 86.6% for Arabidopsis. Compared to other methods, BiLSTM→CNNdemonstrates excellent performance in predicting 6mA sites across the three plant species, and exhibits cross-species generalization capabilities.展开更多
文摘DNA N6-甲基腺嘌呤(6mA)是一种重要的表观遗传修饰,参与基因调控、DNA复制和修复等生物过程,对疾病研究也具有重要意义,准确识别DNA 6mA位点对理解其功能和机制至关重要。尽管现有的NA 6mA位点预测方法已取得较大成功,但在预测精度和跨物种泛化能力上仍有改进空间。本文提出了一种结合双向长短期记忆网络(BiLSTM)和卷积神经网络(CNN)的混合深度学习模型(BiLSTM→CNN)来提高对DNA 6mA位点预测的能力。模型首先采用one-hot、EIIP和DNA二聚体三种编码方式对DNA序列进行编码,然后在不同网络结构、层数和优化器下优化模型。通过在蔷薇科植物、水稻和拟南芥的数据集上的广泛实验表明,BiLSTM→CNN模型在蔷薇科植物中的准确率(ACC)为94.5%,在水稻中为93.8%,在拟南芥中为86.6%。与其他方法相比,BiLSTM→CNN模型在三个植物物种的6mA位点预测中均展现出良好的性能,并具有出色的跨物种泛化能力。DNA N6-methyladenine (6mA) is an important epigenetic modification involved in biological processes such as gene regulation, DNA replication, and repair, making it significant for disease research. Therefore, accurately identifying DNA 6mA sites is crucial for understanding their functions and mechanisms. Despite notable successes with existing methods, there is still room for improvement in prediction accuracy and cross-species generalization. In this study, we propose a hybrid deep learning model (BiLSTM→CNN) that integrates bidirectional long short-term memory networks (BiLSTM) and convolutional neural networks (CNN). Firstly, the model-encoded DNA sequences employ one-hot encoding, EIIP encoding, and DNA dimer encoding. And then optimized under various network architectures, layer configurations and optimizers. We conducted experiments on datasets from Rosaceae, rice and Arabidopsis thaliana, the results indicate that the BiLSTM→CNNmodel achieves an accuracy (ACC) of 94.5% for Rosaceae, 93.8% for rice, and 86.6% for Arabidopsis. Compared to other methods, BiLSTM→CNNdemonstrates excellent performance in predicting 6mA sites across the three plant species, and exhibits cross-species generalization capabilities.
