利用基因本体层次预测基因功能
Using the Gene Ontology Hierarchy when Predicting Gene Function
摘要：
多标签分类的问题时，标签是相关的，通过一个层次的分类方案发生在许多应用领域，如计算生物学。例如，当试图使用诸如Gene Ontology的受控词汇表自动分配基因功能时，自然会出现这个问题。然而，大多数现有的预测基因功能的方法解决了独立的分类问题，以预测参与给定功能类别的基因，独立于其余的。在这里，我们提出了两个简单的方法，将信息的层次性质的分类方案。在第一种方法中，我们使用有关基因先前注释的信息来设置其标签的初始先验。在第二种方法中，我们扩展了基于图的半监督学习算法预测基因功能的层次结构。我们表明，我们可以有效地解决这个问题，通过求解线性方程组。我们比较这些方法与以前的标签和解为基础的方法。结果表明，直接使用层次信息，与使用调和方法相比，提高了基因功能预测。
英文摘要：
The problem of multilabel classification when the labels are related through a hierarchical categorization scheme occurs in many application domains such as computational biology. For example, this problem arises naturally when trying to automatically assign gene function using a controlled vocabularies like Gene Ontology. However, most existing approaches for predicting gene functions solve independent classification problems to predict genes that are involved in a given function category, independently of the rest. Here, we propose two simple methods for incorporating information about the hierarchical nature of the categorization scheme. In the first method, we use information about a gene's previous annotation to set an initial prior on its label. In a second approach, we extend a graph-based semi-supervised learning algorithm for predicting gene function in a hierarchy. We show that we can efficiently solve this problem by solving a linear system of equations. We compare these approaches with a previous label reconciliation-based approach. Results show that using the hierarchy information directly, compared to using reconciliation methods, improves gene function prediction.
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