Current approaches to gene regulatory network modelling
Many different approaches have been developed to model and simulate gene regulatory
networks. We proposed the following categories for gene regulatory network models …
networks. We proposed the following categories for gene regulatory network models …
[HTML][HTML] Current status and trends in forest genomics
Forests are not only the most predominant of the Earth's terrestrial ecosystems, but are also
the core supply for essential products for human use. However, global climate change and …
the core supply for essential products for human use. However, global climate change and …
TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization
Four statistical selection methods for inferring transcription factor (TF)–target gene (TG) pairs
were developed by coupling mean squared error (MSE) or Huber loss function, with elastic …
were developed by coupling mean squared error (MSE) or Huber loss function, with elastic …
TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction
C Gunasekara, K Zhang, W Deng… - Nucleic acids …, 2018 - academic.oup.com
Despite their important roles, the regulators for most metabolic pathways and biological
processes remain elusive. Presently, the methods for identifying metabolic pathway and …
processes remain elusive. Presently, the methods for identifying metabolic pathway and …
Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes
S Kumari, W Deng, C Gunasekara, V Chiang… - BMC …, 2016 - Springer
Background Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very
important for understanding genetics regulation of biological pathways. However, there are …
important for understanding genetics regulation of biological pathways. However, there are …
Approaches to modeling gene regulatory networks: A gentle introduction
T Schlitt - In Silico Systems Biology, 2013 - Springer
This chapter is split into two main sections; first, I will present an introduction to gene
networks. Second, I will discuss various approaches to gene network modeling which will …
networks. Second, I will discuss various approaches to gene network modeling which will …
CaSPIAN: a causal compressive sensing algorithm for discovering directed interactions in gene networks
A Emad, O Milenkovic - PloS one, 2014 - journals.plos.org
We introduce a novel algorithm for inference of causal gene interactions, termed CaSPIAN
(Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on …
(Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on …
Sequential logic model deciphers dynamic transcriptional control of gene expressions
Background Cellular signaling involves a sequence of events from ligand binding to
membrane receptors through transcription factors activation and the induction of mRNA …
membrane receptors through transcription factors activation and the induction of mRNA …
Inferring genetic networks with a recurrent neural network model using differential evolution
In this chapter, we present an evolutionary approach for reverse-engineering gene
regulatory networks (GRNs) from the temporal gene expression profile. The regulatory …
regulatory networks (GRNs) from the temporal gene expression profile. The regulatory …
Behavioral dynamics of bacteriophage gene regulatory networks
We present hybrid system-based gene regulatory network models for lambda, HK022, and
Mu bacteriophages together with dynamics analysis of the modeled networks. The proposed …
Mu bacteriophages together with dynamics analysis of the modeled networks. The proposed …