We introduce in this survey the major concepts, models, and algorithms proposed so far to
infer causal relations from observational time series, a task usually referred to as causal …

Abstract Dynamic Bayesian networks (DBNs) as an extension of traditional Bayesian
networks have recently been paid great concern to environmental modeling to capture …

Transcriptional regulation plays vital roles in many fundamental biological processes.
Reverse engineering of genome-wide regulatory networks from high-throughput …

An essential issue in surrogate model-based reliability analysis is the selection of training
points. Approaches such as efficient global reliability analysis (EGRA) and adaptive Kriging …

The inference of gene regulatory networks (GRNs) from expression data can mine the direct
regulations among genes and gain deep insights into biological processes at a network …

Traffic congestion is a condition of a segment in the road network where the traffic demand is
greater than the available road capacity. The detection of unusual traffic patterns including …

Background Modeling dynamic regulatory networks is a major challenge since much of the
protein-DNA interaction data available is static. The Dynamic Regulatory Events Miner …

We test the adequacies of several proposed and two new statistical methods for recovering
the causal structure of systems with feedback from synthetic BOLD time series. We compare …

Motivation Reconstructing gene regulatory networks (GRNs) based on gene expression
profiles is still an enormous challenge in systems biology. Random forest-based methods …

Bayesian Networks (BN) have been a popular predictive modeling formalism in
bioinformatics, but their application in modern genomics has been slowed by an inability to …