With the advent of the era of big data, it is troublesome to accurately predict the associations between lncRNAs and diseases based on traditional biological experiments due to its time-consuming and subjective. In this paper, we propose a novel deep learning method for predicting lncRNA-disease associations using multi-feature coding and attention convolutional neural network (MCA-Net). We first calculate six similarity features to extract different types of lncRNA and disease feature information. Second, a multi-feature coding method is proposed to construct the feature vectors of lncRNA-disease association samples by integrating the six similarity features. Furthermore, an attention convolutional neural network is developed to identify lncRNA-disease associations under 10-fold cross-validation. Finally, we evaluate the performance of MCA-Net from different perspectives including the effects of the model parameters, distinct deep learning models, and the necessity of attention mechanism. We also compare MCA-Net with several state-of-the-art methods on three publicly available datasets, i.e., LncRNADisease, Lnc2Cancer, and LncRNADisease2.0. The results show that our MCA-Net outperforms the state-of-the-art methods on all three dataset. Besides, case studies on breast cancer and lung cancer further verify that MCA-Net is effective and accurate for the lncRNA-disease association prediction.

中文翻译：

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MCA-Net：用于预测 lncRNA 疾病关联的多特征编码和注意卷积神经网络

随着大数据时代的到来，传统的生物实验由于耗时且具有主观性，难以准确预测lncRNA与疾病之间的关联。在本文中，我们提出了一种新的深度学习方法，用于使用多特征编码和注意力卷积神经网络 (MCA-Net) 预测 lncRNA 与疾病的关联。我们首先计算六个相似特征来提取不同类型的 lncRNA 和疾病特征信息。其次，提出了一种多特征编码方法，通过整合六个相似特征来构建lncRNA-疾病关联样本的特征向量。此外，开发了一个注意力卷积神经网络来识别 10 倍交叉验证下的 lncRNA 疾病关联。最后，我们从不同的角度评估了 MCA-Net 的性能，包括模型参数的影响、不同的深度学习模型以及注意力机制的必要性。我们还在三个公开可用的数据集（即 LncRNADisease、Lnc2Cancer 和 LncRNADisease2.0）上将 MCA-Net 与几种最先进的方法进行了比较。结果表明，我们的 MCA-Net 在所有三个数据集上都优于最先进的方法。此外，乳腺癌和肺癌的案例研究进一步验证了 MCA-Net 对 lncRNA-疾病关联预测的有效性和准确性。Lnc2Cancer 和 LncRNADisease2.0。结果表明，我们的 MCA-Net 在所有三个数据集上都优于最先进的方法。此外，乳腺癌和肺癌的案例研究进一步验证了 MCA-Net 对 lncRNA-疾病关联预测的有效性和准确性。Lnc2Cancer 和 LncRNADisease2.0。结果表明，我们的 MCA-Net 在所有三个数据集上都优于最先进的方法。此外，乳腺癌和肺癌的案例研究进一步验证了 MCA-Net 对 lncRNA-疾病关联预测的有效性和准确性。