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

  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},
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.



Identification of gene interactions associated with disease from gene expression data using synergy networks

BackgroundAnalysis of microarray data has been used for the inference of gene-gene interactions. If, however, the aim is the discovery of disease-related biological mechanisms, then the criterion for

Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data.

Reverse engineering cellular networks

A computational protocol for the ARACNE algorithm, an information-theoretic method for identifying transcriptional interactions between gene products using microarray expression profile data, which envision that predictions made by ARACne, especially when supplemented with prior knowledge or additional data sources, can provide appropriate hypotheses for the further investigation of cellular networks.

How to infer gene networks from expression profiles

It is shown that reverse‐engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements.

Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements.

  • A. ButteI. Kohane
  • Computer Science
    Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
  • 2000
A technique that computes comprehensive pair-wise mutual information for all genes in such a data set and shows how this technique was used on a public data set of 79 RNA expression measurements of 2,467 genes to construct 22 clusters, or Relevance Networks.

Genome-Wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes

The method discovered a set of 100 putative modulator genes, responsible for modulating 205 regulatory relationships between MYC and its targets, which is significantly enriched in molecules with function consistent with their activities as modulators of cellular interactions, recapitulates established MYC regulation pathways, and provides a notable repertoire of novel regulators of MYC function.

The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo

The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data, and successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data.

ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

This approach should enhance the ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.

Correspondence analysis of genes and tissue types and finding genetic links from microarray data.

This paper proposes and uses correspondence analysis which visualizes the relationship between genes and tissues as two 2 dimensional graphs, oriented so that distances between genes, distances between tissues are preserved, and so that genes which primarily distinguish certain types of tissue are spatially close to those tissues.

Reverse engineering of regulatory networks in human B cells

The reconstruction of regulatory networks from expression profiles of human B cells is reported, suggestive of a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions.