FEATURE SELECTION FOR DATA INTEGRATION WITH MIXED MULTIVIEW DATA

• 成果类型: Article
• 署名作者: Baker, Yulia; Tang, Tiffany M.; Allen, Genevera, I
• 署名单位: Rice University; University of California System; University of California Berkeley; Rice University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1389
• 发表日期: 2020
• 页码: 1676-1698
• 关键词: VARIABLE SELECTION; model selection; Graphical Models; Lasso; breast; sparsity; RECOVERY; expression; mutations; risks

摘要

Data integration methods that analyze multiple sources of data simultaneously can often provide more holistic insights than can separate inquiries of each data source. Motivated by the advantages of data integration in the era of big data, we investigate feature selection for high-dimensional multiview data with mixed data types (e.g., continuous, binary, count-valued). This heterogeneity of multiview data poses numerous challenges for existing feature selection methods. However, after critically examining these issues through empirical and theoretically-guided lenses, we develop a practical solution, the Block Randomized Adaptive Iterative Lasso (B-RAIL) which combines the strengths of the randomized Lasso, adaptive weighting schemes and stability selection. B-RAIL serves as a versatile data integration method for sparse regression and graph selection, and we demonstrate the effectiveness of B-RAIL through extensive simulations and a case study to infer the ovarian cancer gene regulatory network. In this case study, B-RAIL successfully identifies well-known biomarkers associated with ovarian cancer and hints at novel candidates for future ovarian cancer research.