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From Knockouts to Networks: Establishing Direct Cause-Effect Relationships through Graph Analysis

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  • Andrea Pinna
  • Nicola Soranzo
  • Alberto de la Fuente

Abstract

Background: Reverse-engineering gene networks from expression profiles is a difficult problem for which a multitude of techniques have been developed over the last decade. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods. Results: We propose an inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges. Substantial improvements on the predictions can be obtained after the execution of this second step. Conclusions: Our algorithm was awarded the best overall performance at the DREAM4 In Silico 100-gene network sub-challenge, proving to be effective in inferring medium-size gene regulatory networks. This success demonstrates once again the decisive importance of gene expression data obtained after systematic gene perturbations and highlights the usefulness of graph analysis to increase the reliability of inference.

Suggested Citation

  • Andrea Pinna & Nicola Soranzo & Alberto de la Fuente, 2010. "From Knockouts to Networks: Establishing Direct Cause-Effect Relationships through Graph Analysis," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-8, October.
    • Handle: RePEc:plo:pone00:0012912
    DOI: 10.1371/journal.pone.0012912
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    References listed on IDEAS

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    1. Eric E. Schadt, 2009. "Molecular networks as sensors and drivers of common human diseases," Nature, Nature, vol. 461(7261), pages 218-223, September.
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    1. Livi, Lorenzo & Maiorino, Enrico & Pinna, Andrea & Sadeghian, Alireza & Rizzi, Antonello & Giuliani, Alessandro, 2016. "Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 199-214.
    2. Atte Aalto & Lauri Viitasaari & Pauliina Ilmonen & Laurent Mombaerts & Jorge Gonçalves, 2020. "Gene regulatory network inference from sparsely sampled noisy data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. Ali Shojaie & Alexandra Jauhiainen & Michael Kallitsis & George Michailidis, 2014. "Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.

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