Hierarchical network analysis of co-occurring bioentities in literature.

Biomedical databases grow by more than a thousand new publications every day. The large volume of biomedical literature that is being published at an unprecedented rate hinders the discovery of relevant knowledge from keywords of interest to gather new insights and form hypotheses. A text-mining tool, PubTator, helps to automatically annotate bioentities, such as species, chemicals, genes, and diseases, from PubMed abstracts and full-text articles.

Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network.

Network-based methods for the analysis of drug-target interactions have gained attention and rely on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze the interactions between the chemicals in the medicinal plants and multiple targets based on the complex multipartite network of the medicinal plants, multi-chemicals, and multiple targets. The multipartite network was constructed via the conjunction of two relationships: chemicals in plants and the biological actions of those chemicals on the targets.

Dynamic functional connectivity analysis based on time-varying partial correlation with a copula-DCC-GARCH model.

We suggest a time-varying partial correlation as a statistical measure of dynamic functional connectivity (dFC) in the human brain. Traditional statistical models often assume specific distributions on the measured data such as the Gaussian distribution, which prohibits their application to neuroimaging data analysis. First, we use the copula-based dynamic conditional correlation (DCC), which does not rely on a specific distribution assumption, for estimating time-varying correlation between regions-of-interest (ROIs) of the human brain.

Dynamic functional connectivity analysis of functional MRI based on copula time-varying correlation.

Background: Recent studies showed that functional connectivity (FC) in the human brain is not static but can dynamically change across time within time scales of seconds to minutes.

New Method: This study introduces a new statistical method called the copula time-varying correlation for dynamic functional connectivity (dFC) analysis from functional magnetic resonance imaging (fMRI) data.

Results: Compared to other state-of-the-art statistical measures of dynamic correlation such as the dynamic conditional correlation (DCC), the proposed method can be effectively applied to data having asymmetric or non-normal distributions.

Directional dependence between major cities in China based on copula regression on air pollution measurements.

Air pollution is well-known as a major risk to public health, causing various diseases including pulmonary and cardiovascular diseases. As social concern increases, the amount of air pollution data is increasing rapidly. The purpose of this study is to statistically characterize dependence between major cities in China based on a measure of directional dependence estimated from PM2.

Copula directional dependence for inference and statistical analysis of whole-brain connectivity from fMRI data.

Introduction: Inferring connectivity between brain regions has been raising a lot of attention in recent decades. Copula directional dependence (CDD) is a statistical measure of directed connectivity, which does not require strict assumptions on probability distributions and linearity.

Methods: In this work, CDDs between pairs of local brain areas were estimated based on the fMRI responses of human participants watching a Pixar animation movie.

An improvement on local FDR analysis applied to functional MRI data.

Background: Discovering effective connectivity between brain regions gained a lot of attention recently. A vector autoregressive model is a simple and flexible approach for exploratory structural modeling where the involvement of a large number of brain regions is crucial to avoid confounding. The non-zero coefficients of the VAR model are interpreted as actual effective connectivity between brain regions.