Inference of Regulatory Gene Interactions from Expression Data Using Three‐Way Mutual Information

@article{Watkinson2009InferenceOR,
  title={Inference of Regulatory Gene Interactions from Expression Data Using Three‐Way Mutual Information},
  author={John Watkinson and Kuo-ching Liang and Xiadong Wang and Tian Zheng and Dimitris Anastassiou},
  journal={Annals of the New York Academy of Sciences},
  year={2009},
  volume={1158}
}
This paper describes the technique designated best performer in the 2nd conference on Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Challenge 5 (unsigned genome-scale network prediction from blinded microarray data. [] Key Result When tested on a set of publicly available Escherichia coli gene-expression data with known assumed ground truth, the synergy augmented CLR (SA-CLR) algorithm had significantly improved prediction performance when compared to CLR.

Gene Regulatory Network Reconstruction Using Conditional Mutual Information

This work proposes a relevance network model for gene regulatory network inference which employs both mutual information and conditional mutual information to determine the interactions between genes and proposes a conditional mutual Information estimator based on adaptive partitioning which allows us to condition on both discrete and continuous random variables.

Gene regulation network inference using k-nearest neighbor-based mutual information estimation-Revisiting an old DREAM

This work shows that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning, and demonstrates that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly using methods.

Wisdom of crowds for robust gene network inference

A comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data defines the performance, data requirements and inherent biases of different inference approaches, and provides guidelines for algorithm application and development.

DREAM3: Network Inference Using Dynamic Context Likelihood of Relatedness and the Inferelator

This work aims to investigate whether scalable information based methods and more explicit dynamical models (like Inferelator 1.0) prove synergistic when combined and whether these can be combined to resolve the directionality of regulatory interactions.

Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures

PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes, is developed and demonstrated that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data.

Inference of gene interaction networks using conserved subsequential patterns from multiple time course gene expression datasets

This work proposes to infer reliable GINs from multiple TCGx datasets using a novel conserved subsequential pattern of gene expression, and demonstrates that the reliable GIns achieve much better prediction performance especially with much higher precision.

A Network Inference Workflow Applied to Virulence‐Related Processes in Salmonella typhimurium

A workflow for the reconstruction of parts of the transcriptional regulatory network of the pathogenic bacterium Salmonella typhimurium based on the information contained in sets of microarray gene‐expression data now available for that organism is discussed.

Causal inference and prior integration in bioinformatics using information theory

A complete computational framework that uses experimental knock down data in a cross-validation scheme to both infer and validate directed networks and identifies the set of genes that was truly affected by the perturbation experiment.
...

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