Chen Sisi, Huang Chiguo, Wang Lei, Zhou Shunxian
The First Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China.
Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China.
Front Genet. 2023 Jan 4;13:1087294. doi: 10.3389/fgene.2022.1087294. eCollection 2022.
Essential proteins play important roles in the development and survival of organisms whose mutations are proven to be the drivers of common internal diseases having higher prevalence rates. Due to high costs of traditional biological experiments, an improved Transfer Neural Network (TNN) was designed to extract raw features from multiple biological information of proteins first, and then, based on the newly-constructed Transfer Neural Network, a novel computational model called TNNM was designed to infer essential proteins in this paper. Different from traditional Markov chain, since Transfer Neural Network adopted the gradient descent algorithm to automatically obtain the transition probability matrix, the prediction accuracy of TNNM was greatly improved. Moreover, additional antecedent memory coefficient and bias term were introduced in Transfer Neural Network, which further enhanced both the robustness and the non-linear expression ability of TNNM as well. Finally, in order to evaluate the identification performance of TNNM, intensive experiments have been executed based on two well-known public databases separately, and experimental results show that TNNM can achieve better performance than representative state-of-the-art prediction models in terms of both predictive accuracies and decline rate of accuracies. Therefore, TNNM may play an important role in key protein prediction in the future.
必需蛋白质在生物体的发育和存活中发挥着重要作用，其突变被证明是常见内科疾病患病率较高的驱动因素。由于传统生物学实验成本高昂，本文设计了一种改进的转移神经网络（TNN），首先从蛋白质的多种生物学信息中提取原始特征，然后基于新构建的转移神经网络，设计了一种名为TNNM的新型计算模型来推断必需蛋白质。与传统马尔可夫链不同，由于转移神经网络采用梯度下降算法自动获取转移概率矩阵，TNNM的预测准确率得到了极大提高。此外，在转移神经网络中引入了额外的先行记忆系数和偏差项，这也进一步增强了TNNM的鲁棒性和非线性表达能力。最后，为了评估TNNM的识别性能，分别基于两个著名的公共数据库进行了大量实验，实验结果表明，在预测准确率和准确率下降率方面，TNNM都能比具有代表性的最新预测模型取得更好的性能。因此，TNNM未来可能在关键蛋白质预测中发挥重要作用。
