Bioinformatics Vol. 18 no. 90001 2002
Pages S216-S224
© 2002 Oxford University Press
Evaluating functional network inference using simulations of complex biological systems
1 Department of Neurobiology,
Duke University Medical Center, Box 3209, Durham, NC 27710,
USA
2 Department of Computer Science,
Duke University, Box 90129, Durham, NC 27708, USA
Received on January 24, 2002
; revised on April 1, 2002
; accepted on April 1, 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.
Availability: Source code and simulated data are available upon request.
Contact: amink{at}cs.duke.edu asmith{at}neuro.duke.edu jarvis{at}neuro.duke.edu
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  A. KUNDAJE, S. LIANOGLOU, X. LI, D. QUIGLEY, M. ARIAS, C. H. WIGGINS, L. ZHANG, and C. LESLIE Learning Regulatory Programs That Accurately Predict Differential Expression with MEDUSA Ann. N.Y. Acad. Sci., December 1, 2007; 1115(1): 178 - 202. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 N. Soranzo, G. Bianconi, and C. Altafini Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data Bioinformatics, July 1, 2007; 23(13): 1640 - 1647. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. V. Werhli, M. Grzegorczyk, and D. Husmeier Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks Bioinformatics, October 15, 2006; 22(20): 2523 - 2531. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
X.-w. Chen, G. Anantha, and X. Wang An effective structure learning method for constructing gene networks Bioinformatics, June 1, 2006; 22(11): 1367 - 1374. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. Danks Scientific Coherence and the Fusion of Experimental Results Brit J Philos Sci, December 1, 2005; 56(4): 791 - 807. [Abstract] [Full Text] [PDF]
|
|