A New Approach to Large Multiomics Data Integration.

ABSTRACT: Data reduction and data mining are common practices for handling large-scale data from wide-ranging sources, but high-dimensional omics and imaging data sets present difficult challenges for feature extraction and data mining due to the large number of features that cannot be simultaneously examined. The sample numbers and variables in these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. In recent years, there has been a rapid uptake of nonlinear dimensionality reduction via methods such as t-distributed stochastic neighbor embedding and uniform manifold approximation and projection. These approaches have revolutionized our ability to visualize and interpret high-dimensional data and have rapidly

SUBMITTER: Dexter A 

PROVIDER: S-EPMC12461686 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature