Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

ABSTRACT: Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general "linear" multivariate analysis alone limits interpretations due to "non-linear" variations in metabolic data from living organisms. Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data. To overcome the limitation of important variable (metabolite) determinations, we incorporated a random forest conditional v

SUBMITTER: Shiokawa Y 

PROVIDER: S-EPMC5821832 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature