English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The mainstream of research in genetics, epigenetics and imaging data analysisfocuses on statistical association or exploring statistical dependence betweenvariables. 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 dataanalysis is a key issue that hampers the theoretic development of genomicscience and its application in practice. Causal inference is an essentialcomponent for the discovery of mechanical relationships among complexphenotypes. Many researchers suggest making the transition from association tocausation. Despite its fundamental role in science, engineering andbiomedicine, the traditional methods for causal inference require at leastthree variables. However, quantitative genetic analysis such as QTL, eQTL,mQTL, and genomic-imaging data analysis requires exploring the causalrelationships between two variables. This paper will focus on bivariate causaldiscovery. We will introduce independence of cause and mechanism (ICM) as abasic principle for causal inference, algorithmic information theory andadditive noise model (ANM) as major tools for bivariate causal discovery.Large-scale simulations will be performed to evaluate the feasibility of theANM for bivariate causal discovery. To further evaluate their performance forcausal inference, the ANM will be applied to the construction of generegulatory networks. Also, the ANM will be applied to trait-imaging dataanalysis to illustrate three scenarios: presence of both causation andassociation, presence of association while absence of causation, and presenceof causation, while lack of association between two variables.

Bivariate Causal Discovery and its Applications to Gene Expression and Imaging Data Analysis