面向蛋白质功能预测中有向无环图标记结构的多示例多标记学习

在多示例多标记学习问题中,标记之间往往是相互关联的,其中有向无环图结构是一种常见的层次关联结构,可见于蛋白质的基因本体学生物学功能预测的应用场景中。针对其标记间的有向无环图结构,提出了一种新的多示例多标记学习算法。算法从原始数据的特征空间训练出所有标记共享的低维子空间,通过随机梯度下降方法来降低模型排序损失,并融入标记间有向无环图结构关系对预测标记进行优化。将该算法应用于多个数据集的蛋白质功能预测中,实验结果表明,该算法具有更高的效率及预测性能。

Multi-instance multi-label learning for labels with directed acyclic graph structures in protein function prediction

In MIML (multi-instance multi-label learning) tasks, labels are often correlated with each other, and DAG (directed acyclic graph) is a common hierarchically structure which often occurs in the prediction of gene ontology biological functions of proteins. Considering the labels with directed acyclic graph structures in MIML, a novel algorithm named MIMLDAG (multi-instance multi-label directed acyclic graph) was proposed. MIMLDAG trained a low-dimensional subspace of shared labels from the feature space of original datasets, minimized the rank loss by a stochastic gradient descent method, and then incorporated the inner DAG hierarchical structure of labels for optimizing the output labels. MIMLDAG was applied to predict the protein functions in multiple datasets, and the results show that MIMLDAG possesses higher efficiency and predictive performance.