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Evaluating functional network inference using simulations of complex biological systems.

Publication ,  Journal Article
Smith, VA; Jarvis, ED; Hartemink, AJ
Published in: Bioinformatics
2002

MOTIVATION: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data. RESULTS: We designed a simulator to generate data representing a complex biological system at multiple levels of organization: behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds. About 90% of the simulated variables are unregulated by other variables in the system and are included simply as distracters. We sampled the simulated data at intervals as one would sample from a biological system in practice, and then used the sampled data to evaluate the effectiveness of an algorithm we developed for functional network inference. We found that our algorithm is highly effective at recovering the functional network structure of the simulated system-including the irrelevance of unregulated variables-from sampled data alone. To assess the reproducibility of these results, we tested our inference algorithm on 50 separately simulated sets of data and it consistently recovered almost perfectly the complex functional network structure underlying the simulated data. To our knowledge, this is the first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data. Our simulation approach also enables researchers a priori to design experiments and data-collection protocols that are amenable to functional network inference.

Duke Scholars

Published In

Bioinformatics

DOI

ISSN

1367-4803

Publication Date

2002

Volume

18 Suppl 1

Start / End Page

S216 / S224

Location

England

Related Subject Headings

  • Vocalization, Animal
  • Songbirds
  • Signal Transduction
  • Models, Neurological
  • Gene Expression Regulation
  • Gene Expression Profiling
  • Computer Simulation
  • Brain
  • Bioinformatics
  • Animals
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Smith, V. A., Jarvis, E. D., & Hartemink, A. J. (2002). Evaluating functional network inference using simulations of complex biological systems. Bioinformatics, 18 Suppl 1, S216–S224. https://doi.org/10.1093/bioinformatics/18.suppl_1.s216
Smith, V Anne, Erich D. Jarvis, and Alexander J. Hartemink. “Evaluating functional network inference using simulations of complex biological systems.Bioinformatics 18 Suppl 1 (2002): S216–24. https://doi.org/10.1093/bioinformatics/18.suppl_1.s216.
Smith VA, Jarvis ED, Hartemink AJ. Evaluating functional network inference using simulations of complex biological systems. Bioinformatics. 2002;18 Suppl 1:S216–24.
Smith, V. Anne, et al. “Evaluating functional network inference using simulations of complex biological systems.Bioinformatics, vol. 18 Suppl 1, 2002, pp. S216–24. Pubmed, doi:10.1093/bioinformatics/18.suppl_1.s216.
Smith VA, Jarvis ED, Hartemink AJ. Evaluating functional network inference using simulations of complex biological systems. Bioinformatics. 2002;18 Suppl 1:S216–S224.
Journal cover image

Published In

Bioinformatics

DOI

ISSN

1367-4803

Publication Date

2002

Volume

18 Suppl 1

Start / End Page

S216 / S224

Location

England

Related Subject Headings

  • Vocalization, Animal
  • Songbirds
  • Signal Transduction
  • Models, Neurological
  • Gene Expression Regulation
  • Gene Expression Profiling
  • Computer Simulation
  • Brain
  • Bioinformatics
  • Animals