Chen Yuchi, Xie Minzhu, Wen Jie
College of Information Science and Engineering, Hunan Normal University, Changsha, China.
Front Genet. 2022 Dec 14;13:1081842. doi: 10.3389/fgene.2022.1081842. eCollection 2022.
It is well known that histone modifications play an important part in various chromatin-dependent processes such as DNA replication, repair, and transcription. Using computational models to predict gene expression based on histone modifications has been intensively studied. However, the accuracy of the proposed models still has room for improvement, especially in cross-cell lines gene expression prediction. In the work, we proposed a new model TransferChrome to predict gene expression from histone modifications based on deep learning. The model uses a densely connected convolutional network to capture the features of histone modifications data and uses self-attention layers to aggregate global features of the data. For cross-cell lines gene expression prediction, TransferChrome adopts transfer learning to improve prediction accuracy. We trained and tested our model on 56 different cell lines from the REMC database. The experimental results show that our model achieved an average Area Under the Curve (AUC) score of 84.79%. Compared to three state-of-the-art models, TransferChrome improves the prediction performance on most cell lines. The experiments of cross-cell lines gene expression prediction show that TransferChrome performs best and is an efficient model for predicting cross-cell lines gene expression.
众所周知，组蛋白修饰在各种依赖染色质的过程中发挥着重要作用，如DNA复制、修复和转录。基于组蛋白修饰使用计算模型预测基因表达已得到深入研究。然而，所提出模型的准确性仍有提升空间，尤其是在跨细胞系基因表达预测方面。在这项工作中，我们提出了一种新的模型TransferChrome，用于基于深度学习从组蛋白修饰预测基因表达。该模型使用密集连接卷积网络来捕捉组蛋白修饰数据的特征，并使用自注意力层来聚合数据的全局特征。对于跨细胞系基因表达预测，TransferChrome采用迁移学习来提高预测准确性。我们在来自REMC数据库的56种不同细胞系上对我们的模型进行了训练和测试。实验结果表明，我们的模型平均曲线下面积（AUC）得分达到了84.79%。与三种最先进的模型相比，TransferChrome在大多数细胞系上提高了预测性能。跨细胞系基因表达预测实验表明，TransferChrome表现最佳，是一种预测跨细胞系基因表达的有效模型。
