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ACE specifications

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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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Maintainer


  • person_outline John Barton

Publications for Adaptive Cluster Expansion

ACE citations

 (3)
library_books

Optimal structure and parameter learning of Ising models

2018
Sci Adv
PMCID: 5856491
PMID: 29556527
DOI: 10.1126/sciadv.1700791

[…] putational 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, and the probabili […]

library_books

Approximate Inference for Time Varying Interactions and Macroscopic Dynamics of Neural Populations

2017
PLoS Comput Biol
PMCID: 5283755
PMID: 28095421
DOI: 10.1371/journal.pcbi.1005309

[…] out 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 and […]

library_books

Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models

2016
PLoS Comput Biol
PMCID: 4866778
PMID: 27177270
DOI: 10.1371/journal.pcbi.1004889

[…] our 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 to infer […]


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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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