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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Generalized Query-Based Active Learning to Identify Differentially Methylated Regions in DNA
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Generalized Query-Based Active Learning to Identify Differentially Methylated Regions in DNA

机译：基于广义查询的主动学习，以识别DNA中的甲基化差异区域

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摘要

Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.
机译：主动学习是一种有监督的学习技术，可减少构建成功的分类器所需的示例数量，因为它可以选择从中学习的数据。该技术在许多生物学领域都具有希望，在这些领域中，获得分类的实例既昂贵又费时。大多数传统的主动学习方法都会向Oracle（例如人类专家）提出非常具体的查询，以标记未标记的示例。该示例可能包含许多功能，其中许多都是不相关的。删除此类功能将创建仅包含相关功能的较短查询，并且Oracle易于回答。我们提出了一种基于通用查询的主动学习（GQAL）方法，该方法基于多个实例构造了通用查询。通过构造适当的广义查询，与传统的主动学习方法相比，我们可以获得更高的准确性。我们应用主动学习方法来找到差异DNA甲基化区域（DMR）。 DMR是基因组中的DNA位置，已知与组织分化，表观遗传调控和疾病有关。我们还将我们的方法应用于其他13个数据集，并表明我们的方法比另一种流行的主动学习技术要好。

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