A hierarchical multi-label classification method based on neural networks for gene function prediction
A hierarchical multi-label classification method based on neural networks for gene function prediction
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DOI：
10.1080/13102818.2018.1521302
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发表时间：
2018-11-02
影响因子：
1.4
通讯作者：
Zheng, Wenbin
中科院分区：
文献类型：
--
作者：
Feng, Shou;Fu, Ping;Zheng, Wenbin
基因功能预测是将生物学或生化学功能分配给基因的一种方法，一直是现代生物学中的一个具有挑战性的问题。基因可以同时表现出许多功能，并且这些功能被组织成层次结构，例如基因本体（GO）的有向无环图（DAG）。由于这些特点，基因功能预测可以被看作是一个典型的分层多标签分类（HMC）任务。提出了一种基于神经网络的HMC方法，用于基于GO的基因功能预测。所提出的方法属于一个本地的方法，通过转移的HMC任务的一组子任务。在该方法中实施了三种策略来提高其性能。首先，针对每个类的训练数据集建立时的不平衡问题，采用负实例选择策略和SMOTE方法对每个不平衡训练数据集进行预处理。其次，一个特定的多层感知器（MLP）的设计为每个节点在GO。第三，使用基于贝叶斯网络的后处理方法，以保证结果与层次约束一致。实验结果表明，所提出的HMC-MLPN方法是一种很有前途的基因功能预测方法的基础上，与其他两个国家的最先进的方法进行比较。
Gene function prediction is used to assign biological or biochemical functions to genes, which continues to be a challenging problem in modern biology. Genes may exhibit many functions simultaneously, and these functions are organized into a hierarchy, such as a directed acyclic graph (DAG) for Gene Ontology (GO). Because of these characteristics, gene function prediction can be seen as a typical hierarchical multi-label classification (HMC) task. A novel HMC method based on neural networks is proposed in this article for predicting gene function based on GO. The proposed method belongs to a local approach by transferring the HMC task to a set of subtasks. There are three strategies implemented in this method to improve its performance. First, to tackle the imbalanced data set problem when building the training data set for each class, negative instances selecting policy and SMOTE approach are used to preprocess each imbalanced training data set. Second, a particular multi-layer perceptron (MLP) is designed for each node in GO. Third, a post processing method based on the Bayesian network is used to guarantee that the results are consistent with the hierarchy constraint. The experimental results indicate that the proposed HMC-MLPN method is a promising method for gene function prediction based on a comparison with two other state-of-the-art methods.