| Literature DB >> 34236669 |
Mengyun Wu1, Yu Jiang2, Shuangge Ma3.
Abstract
In recent biomedical studies, multidimensional profiling, which collects proteomics as well as other types of omics data on the same subjects, is getting increasingly popular. Proteomics, transcriptomics, genomics, epigenomics, and other types of data contain overlapping as well as independent information, which suggests the possibility of integrating multiple types of data to generate more reliable findings/models with better classification/prediction performance. In this chapter, a selective review is conducted on recent data integration techniques for both unsupervised and supervised analysis. The main objective is to provide the "big picture" of data integration that involves proteomics data and discuss the "intuition" beneath the recently developed approaches without invoking too many mathematical details. Potential pitfalls and possible directions for future developments are also discussed.Entities:
Keywords: Data integration; High-dimensional statistics; Unsupervised and supervised analysis
Mesh:
Year: 2021 PMID: 34236669 DOI: 10.1007/978-1-0716-1641-3_18
Source DB: PubMed Journal: Methods Mol Biol ISSN: 1064-3745