Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok 10800, Thailand.
Intelligent and Nonlinear Dynamic Innovations Research Center, Science and Technology Research Institute, King Mongkut's University of Technology North Bangkok, Bangkok 10800, Thailand.
Int J Mol Sci. 2021 Sep 16;22(18):10019. doi: 10.3390/ijms221810019.
Functional annotation of unknown function genes reveals unidentified functions that can enhance our understanding of complex genome communications. A common approach for inferring gene function involves the ortholog-based method. However, genetic data alone are often not enough to provide information for function annotation. Thus, integrating other sources of data can potentially increase the possibility of retrieving annotations. Network-based methods are efficient techniques for exploring interactions among genes and can be used for functional inference. In this study, we present an analysis framework for inferring the functions of genes based on connection profiles in a heterogeneous network between human and proteins. These profiles were fed into a hybrid deep learning algorithm to predict the orthologs of unknown function genes. The results show high performance of the model's predictions, with an AUC of 0.89. One hundred and twenty-one predicted pairs with high prediction scores were selected for inferring the functions using statistical enrichment analysis. Using this method, and were found to be involved with muscle contraction and striated muscle tissue development, while and were found to be related to protein dephosphorylation. In conclusion, combining a heterogeneous network and a hybrid deep learning technique can allow us to identify unknown gene functions of malaria parasites. This approach is generalized and can be applied to other diseases that enhance the field of biomedical science.
功能注释未知功能基因揭示了未被识别的功能，这可以增强我们对复杂基因组通讯的理解。一种常用的推断基因功能的方法是基于直系同源的方法。然而，仅靠遗传数据通常不足以提供功能注释的信息。因此，整合其他来源的数据可以增加检索注释的可能性。基于网络的方法是探索基因之间相互作用的有效技术，可用于功能推断。在这项研究中，我们提出了一种基于人类和蛋白质之间的异质网络连接谱来推断基因功能的分析框架。这些谱被输入到一种混合深度学习算法中，以预测未知功能基因的直系同源物。结果表明，该模型的预测性能表现良好，AUC 为 0.89。选择了 121 对具有高预测分数的预测对，使用统计富集分析来推断功能。使用这种方法，发现和与肌肉收缩和横纹肌组织发育有关，而和与蛋白质去磷酸化有关。总之，结合异质网络和混合深度学习技术，可以帮助我们识别疟原虫的未知基因功能。这种方法具有通用性，可以应用于其他增强生物医学科学领域的疾病。
