2016/08/14 by Makoto Yamada, Jiliang Tang, Yamada, Makoto +23 · 1 citation
Biochemistry, Genetics and Molecular Biology · Chemistry · #Advanced Proteomics Techniques and Applications #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning in Bioinformatics #Metabolomics and Mass Spectrometry Studies
paper · pdf · doi:10.48550/arxiv.1608.04048
openalex publication_date 2016/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.