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ACE specifications

Information


Unique identifier OMICS_15298
Name ACE
Alternative name Adaptive Cluster Expansion
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data A set of correlations.
Input format FASTA
Output data Ising or Potts model
Operating system Unix/Linux, Mac OS, Windows
Programming languages Python
License MIT License
Computer skills Advanced
Version 1.0
Stability Stable
Maintained Yes

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  • person_outline John Barton <>

Publication for Adaptive Cluster Expansion

ACE in publications

 (4)
PMCID: 5856491
PMID: 29556527
DOI: 10.1126/sciadv.1700791

[…] complexity. several heuristic algorithms that use higher-order moments have been proposed on the basis of statistical physics arguments. among other approximate methods, let us mention the adaptive cluster expansion (), which controls the accuracy of the approximation at a cost of a higher computational complexity involving computation of entropies of growing clusters, […]

PMCID: 5283755
PMID: 28095421
DOI: 10.1371/journal.pcbi.1005309

[…] the prior, we may use alternative approximation methods such as bethe and tap approximations, and further state-of-the-art methods such as the sessak-monasson [], minimum-probability-flow [], and adaptive-cluster expansion [] method. however, here we chose the pseudolikelihood method because it was not trivial to apply the other methods to the bayesian estimation. alternatively, the bethe […]

PMCID: 4866778
PMID: 27177270
DOI: 10.1371/journal.pcbi.1004889

[…] structures sa, sb, sc, sd, based on the ranking of the mutual information (mi) scores [] and of the inferred potts couplings, with the mean field (dca) [], the pseudo likelihood (plm) [], and the adaptive cluster expansion (ace) [–] procedures. a fifth method, called projection, shown with magenta lines in will be introduced later on. mean-field dca is a very fast, approximate method […]

PMCID: 3240328
DOI: 10.1186/1471-2202-12-S1-P224

[…] a new and efficient algorithm to infer fields and pairwise couplings of an ising model from the data. our procedure considerably improves over the algorithm presented in [] and is based on an adaptive cluster expansion of the cross entropy between the ising model and the data. the interaction network is progressively unveiled, through a recursive processing of larger and larger subsets […]


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ACE institution(s)
Departments of Chemical Engineering and Physics, Massachusetts Institute of Technology, Cambridge, MA, USA; Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA; Laboratoire de Physique Statistique de L’Ecole Normale Supérieure, Ecole Normale Supérieure & Université P.&M. Curie, Paris, France; Computational and Quantitative Biology, UPMC, UMR 7238, Sorbonne Université, Paris, France; Laboratoire de Physique Théorique de L’Ecole Normale Supérieure, Ecole Normale Supérieure & Université P.&M. Curie, Paris, France
ACE funding source(s)
This work was funded by the Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard, by the Institute des Systemes Complexes (ISC-PIF), by the Region Ile-de-France and by ANR (13-BS04-0012-01).

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