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How to infer gene networks from expression profiles.

ABSTRACT: Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse-engineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show 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. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.

SUBMITTER: Bansal M

PROVIDER: S-EPMC1828749 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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How to infer gene networks from expression profiles.

Bansal Mukesh M Belcastro Vincenzo V Ambesi-Impiombato Alberto A di Bernardo Diego D

Molecular systems biology 20070213

Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse-engineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactio ...[more]

PMID: 17299415

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How to infer gene networks from expression profiles

Project description:Correction to: Molecular Systems Biology 3:78. doi:10.1038/msb4100120; Published online 13 February 2007

| S-EPMC1911200 | biostudies-literature

KBoost: a new method to infer gene regulatory networks from gene expression data.

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| S-EPMC8322418 | biostudies-literature

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| S-EPMC5718405 | biostudies-literature

FastMEDUSA: a parallelized tool to infer gene regulatory networks.

Project description:In order to construct gene regulatory networks of higher organisms from gene expression and promoter sequence data efficiently, we developed FastMEDUSA. In this parallelized version of the regulatory network-modeling tool MEDUSA, expression and sequence data are shared among a user-defined number of processors on a single multi-core machine or cluster. Our results show that FastMEDUSA allows a more efficient utilization of computational resources. While the determination of a regulatory network of brain tumor in Homo sapiens takes 12 days with MEDUSA, FastMEDUSA obtained the same results in 6 h by utilizing 100 processors.Source code and documentation of FastMEDUSA are available at https://wiki.nci.nih.gov/display/NOBbioinf/FastMEDUSA

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Recursive regularization for inferring gene networks from time-course gene expression profiles.

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| S-EPMC2686685 | biostudies-literature

TimeXNet: identifying active gene sub-networks using time-course gene expression profiles.

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| S-EPMC5870849 | biostudies-literature

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