Bivariate Causal Discovery and Its Applications to Gene Expression and Imaging Data Analysis.

ABSTRACT: The mainstream of research in genetics, epigenetics, and imaging data analysis focuses on statistical association or exploring statistical dependence between variables. Despite their significant progresses in genetic research, understanding the etiology and mechanism of complex phenotypes remains elusive. Using association analysis as a major analytical platform for the complex data analysis is a key issue that hampers the theoretic development of genomic science and its application in practice. Causal inference is an essential component for the discovery of mechanical relationships among complex phenotypes. Many researchers suggest making the transition from association to causation. Despite its fundamental role in science, engineering, and biomedicine, the traditional methods for causal

SUBMITTER: Jiao R 

PROVIDER: S-EPMC6127271 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature