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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Predicting Protein Function Using Multiple Kernels
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Predicting Protein Function Using Multiple Kernels

机译：使用多个内核预测蛋白质功能

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摘要

High-throughput experimental techniques provide a wide variety of heterogeneous proteomic data sources. To exploit the information spread across multiple sources for protein function prediction, these data sources are transformed into kernels and then integrated into a composite kernel. Several methods first optimize the weights on these kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these approaches result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some approaches optimize the loss of classifiers and learn weights for the different kernels iteratively. For multi-class or multi-label data, these methods have to solve the problem of optimizing weights on these kernels for each of the labels, which are computationally expensive and ignore the correlation among labels. In this paper, we propose a method called Predicting tein Function using ultiple ernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reduces the empirical loss of multi-label classifier for each of the labels simultaneously. ProMK can integrate kernels selectively and downgrade the weights on noisy kernels. We investigate the performance of ProMK on several publicly available protein function prediction benchmarks and synthetic datasets. We show that the proposed approach performs better than previously proposed protein function prediction approaches that integrate multiple data sources and multi-label multiple kernel learning methods. The codes of our proposed method are available at https://sites.google.com/site/guoxian85/promk.
机译：高通量实验技术提供了各种各样的异构蛋白质组数据源。为了利用分布在多个源上的信息进行蛋白质功能预测，将这些数据源转换为内核，然后集成到复合内核中。几种方法首先优化这些内核的权重以生成复合内核，然后在复合内核上训练分类器。这样，这些方法导致最佳的复合内核，但不一定导致最佳的分类器。另一方面，一些方法优化了分类器的损失，并迭代地学习了不同内核的权重。对于多类或多标签数据，这些方法必须解决为每个标签优化这些内核上的权重的问题，这在计算上是昂贵的，并且忽略了标签之间的相关性。在本文中，我们提出了一种使用多能神经（ProMK）预测肌腱功能的方法。 ProMK迭代优化学习最佳权重的阶段，并同时减少每个标签的多标签分类器的经验损失。 ProMK可以有选择地集成内核，并降低嘈杂内核的权重。我们调查了ProMK在几个公开可用的蛋白质功能预测基准和合成数据集上的性能。我们表明，所提出的方法比先前提出的蛋白质功能预测方法表现更好，后者结合了多个数据源和多标签多核学习方法。我们提议的方法的代码可从https://sites.google.com/site/guoxian85/promk获得。

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