2018/12/06 by Hao-Ting Wang, Wang, Hao-Ting, Jonathan Smallwood +12
Biochemistry, Genetics and Molecular Biology · Computer Science · #Applications (stat.AP) #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.1812.02598
openalex publication_date 2018/12/06 · openalex created_date 2022/08/01 · openalex updated_date 2026/07/28
Since the beginning of the 21st century, the size, breadth, and granularity\nof data in biology and medicine has grown rapidly. In the example of\nneuroscience, studies with thousands of subjects are becoming more common,\nwhich provide extensive phenotyping on the behavioral, neural, and genomic\nlevel with hundreds of variables. The complexity of such big data repositories\noffer new opportunities and pose new challenges to investigate brain,\ncognition, and disease. Canonical correlation analysis (CCA) is a prototypical\nfamily of methods for wrestling with and harvesting insight from such rich\ndatasets. This doubly-multivariate tool can simultaneously consider two\nvariable sets from different modalities to uncover essential hidden\nassociations. Our primer discusses the rationale, promises, and pitfalls of CCA\nin biomedicine.\n