Supervised learning is an accurate method for network-based gene classification
Supervised learning is an accurate method for network-based gene classification
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DOI：
10.1093/bioinformatics/btaa150
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发表时间：
2020-06-01
期刊：
影响因子：
5.8
通讯作者：
Krishnan, Arjun
中科院分区：
文献类型：
--
作者：
Liu, Renming;Mancuso, Christopher A.;Krishnan, Arjun
研究背景：研究人类基因的功能、疾病和性状是现代遗传学面临的重大挑战。解决这一挑战的关键是计算方法，如监督学习和标签传播，可以利用分子相互作用网络来预测基因属性。尽管监督学习是一种跨领域的流行机器学习技术，但它仅应用于少数基于网络的研究，用于预测途径，表型或疾病相关基因。目前尚不清楚监督学习如何在不同的网络和不同的基因分类任务中广泛执行，以及它如何与标签传播进行比较，标签传播是该问题的广泛基准规范方法。在这项研究中，我们提出了一个全面的基准监督学习基于网络的基因分类，使用严格的评估方案，在数百个不同的预测任务和多个网络上评估这种方法和经典的标签传播技术。我们证明了基因的全网络连接的监督学习优于标签传播，并通过有效地捕获本地网络属性，达到高预测精度，媲美标签传播的自然使用网络拓扑的吸引力。我们进一步表明，在整个网络上的监督学习也上级于节点嵌入学习（使用node2vec导出），这是一种越来越流行的简洁表示网络连通性的方法。这些结果表明，监督学习是一种准确的方法，可以优先考虑与不同功能，疾病和特征相关的基因，并且应该被视为基于网络的基因分类工作流程的主要内容。
Background: Assigning every human gene to specific functions, diseases and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods, such as supervised learning and label propagation, that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine-learning technique across fields, supervised learning has been applied only in a few network-based studies for predicting pathway-, phenotype- or disease-associated genes. It is unknown how supervised learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label propagation, the widely benchmarked canonical approach for this problem.Results: In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised learning on a gene's full network connectivity outperforms label propagaton and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label propagation's appeal for naturally using network topology. We further show that supervised learning on the full network is also superior to learning on node embeddings (derived using node2vec), an increasingly popular approach for concisely representing network connectivity. These results show that supervised learning is an accurate approach for prioritizing genes associated with diverse functions, diseases and traits and should be considered a staple of network-based gene classification workflows.