Feng Shou, Zhao Chunhui, Fu Ping
College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China.
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China.
Rev Sci Instrum. 2020 Feb 1;91(2):024103. doi: 10.1063/1.5141161.
With the accumulation of data generated by biological experimental instruments, using hierarchical multi-label classification (HMC) methods to process these data for gene function prediction has become very important. As the structure of the widely used Gene Ontology (GO) annotation is the directed acyclic graph (DAG), GO based gene function prediction can be changed to the HMC problem for the DAG of GO. Due to HMC, algorithms for tree ontology are not applicable to DAG, and the accuracy of these algorithms is low. Therefore, existing algorithms cannot satisfy the requirements of gene function prediction. To solve this problem, this paper proposes a DAG hierarchical multi-label classification algorithm, C2AE-DAGLabel algorithm. The C2AE-DAGLabel algorithm uses the Canonical Correlated AutoEncoder (C2AE) model as the classifier and designs a DAGLabel algorithm to solve the DAG hierarchical constraint problem. The DAGLabel algorithm can improve the classification accuracy by ensuring that the classification results meet the requirements of the hierarchical constraint. In the experiment, human gene data annotated with GO are used to evaluate the performance of the proposed algorithm. The experimental results show that compared with other state-of-the-art algorithms, the C2AE-DAGLabel algorithm has the best performance in solving the hierarchical multi-label classification problem for DAG.
随着生物实验仪器所产生数据的积累，使用层次多标签分类（HMC）方法来处理这些数据以进行基因功能预测变得非常重要。由于广泛使用的基因本体（GO）注释的结构是有向无环图（DAG），基于GO的基因功能预测可以转变为针对GO的DAG的HMC问题。由于HMC，用于树状本体的算法不适用于DAG，并且这些算法的准确性较低。因此，现有算法无法满足基因功能预测的要求。为了解决这个问题，本文提出了一种DAG层次多标签分类算法，即C2AE-DAGLabel算法。C2AE-DAGLabel算法使用典型相关自动编码器（C2AE）模型作为分类器，并设计了一种DAGLabel算法来解决DAG层次约束问题。DAGLabel算法可以通过确保分类结果满足层次约束要求来提高分类准确性。在实验中，使用带有GO注释的人类基因数据来评估所提出算法的性能。实验结果表明，与其他现有最先进算法相比，C2AE-DAGLabel算法在解决DAG的层次多标签分类问题方面具有最佳性能。
