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Combining Multiple Results of a Reverse‐Engineering Algorithm: Application to the DREAM Five‐Gene Network Challenge
Author(s) -
Marbach Daniel,
Mattiussi Claudio,
Floreano Dario
Publication year - 2009
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/j.1749-6632.2008.03945.x
Subject(s) - reverse engineering , computer science , reliability (semiconductor) , algorithm , voting , point (geometry) , data mining , artificial intelligence , machine learning , mathematics , power (physics) , physics , geometry , quantum mechanics , politics , political science , law , programming language
The output of reverse‐engineering methods for biological networks is often not a single network prediction, but an ensemble of networks that are consistent with the experimentally measured data. In this paper, we consider the problem of combining the information contained within such an ensemble in order to (1) make more accurate network predictions and (2) estimate the reliability of these predictions. We review existing methods, discuss their limitations, and point out possible research directions toward more advanced methods for this purpose. The potential of considering ensembles of networks, rather than individual inferred networks, is demonstrated by showing how an ensemble voting method achieved winning performance on the Five‐Gene Network Challenge of the second DREAM conference (Dialogue on Reverse Engineering Assessments and Methods 2007, New York, NY).