生物大数据的超高维非线性特征选择
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
摘要：
机器学习方法用于发现生物和医学数据中复杂的非线性关系。然而，复杂的学习模型对于具有数百万特征的数据在计算上是不可行的。在这里，我们介绍了第一种用于非线性学习问题的特征选择方法，该方法可以扩展到大型超高维生物数据。更具体地说，我们扩大了新颖的希尔伯特-施密特独立准则套索（HSIC套索），以处理数以百万计的功能与数万个样本。所提出的方法是保证找到一个最大的预测功能的最佳子集具有最小的冗余，产生更高的预测能力和改善的解释性。它的有效性是通过应用程序来分类表型的基础上模块表达在人类前列腺癌患者和蛋白质结构中检测酶。我们实现了高精度，只需百万分之二十的特征-降维99.998%。我们的算法可以在商品云计算平台上实现。功能的急剧减少可能会导致移动医疗保健应用程序中无处不在的复杂预测模型的部署。
英文摘要：
Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible for data with millions of features. Here we introduce the first feature selection method for nonlinear learning problems that can scale up to large, ultra-high dimensional biological data. More specifically, we scale up the novel Hilbert-Schmidt Independence Criterion Lasso (HSIC Lasso) to handle millions of features with tens of thousand samples. The proposed method is guaranteed to find an optimal subset of maximally predictive features with minimal redundancy, yielding higher predictive power and improved interpretability. Its effectiveness is demonstrated through applications to classify phenotypes based on module expression in human prostate cancer patients and to detect enzymes among protein structures. We achieve high accuracy with as few as 20 out of one million features --- a dimensionality reduction of 99.998%. Our algorithm can be implemented on commodity cloud computing platforms. The dramatic reduction of features may lead to the ubiquitous deployment of sophisticated prediction models in mobile health care applications.
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