ACE: Adaptive cluster expansion for maximum entropy graphical model inference

Bioinformatics

Volumn 32, Issue 20, 2016, Pages 3089-3097

  Barton, J P ^a   De Leonardis, E ^b   Coucke, A ^b,c   Cocco, S ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b SORBONNE UNIVERSITÉ   (France)

^c LABORATOIRE DE PHYSIQUE STATISTIQUE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ENTROPY; NORMAL DISTRIBUTION; STATISTICAL MODEL; THEORETICAL MODEL;

ALGORITHMS; ENTROPY; LIKELIHOOD FUNCTIONS; MODELS, THEORETICAL; NORMAL DISTRIBUTION;

EID: 84991587358     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw328     Document Type: Article

References (43)

1. Ackley, D., et al. (1985). A learning algorithm for Boltzmann machines. Cognit. Sci., 9, 147-169
2. Amari, S. (1987). Differential geometrical theory of statistics. IMS Monograph vol. 10, Differential Geometry in Statistical Inference, pp. 20-94
3. Aurell, E., and Ekeberg, M. (2012). Inverse Ising inference using all the data. Phys. Rev. Lett., 108, 090201
4. Barton, J., and Cocco, S. (2013). Ising models for neural activity inferred via selective cluster expansion: structural and coding properties. J. Stat. Mech.: Theory Expe., 2013, P03002
5. Barton, J.P., et al. (2014). Large pseudocounts and L2-norm penalties are necessary for the mean-field inference of Ising and Potts models. Phys. Rev. E, 90, 012132
6. Barton, J.P., et al. (2015). Scaling laws describe memories of host-pathogen riposte in the HIV population. Proc. Natl. Acad. Sci. U. S. A., 112, 1965-1970
7. Barton, J.P., et al. (2016). On the entropy of protein families. J. Stat. Phys., 162, 1-27
8. Cocco, S., and Monasson, R. (2011). Adaptive cluster expansion for inferring Boltzmann machines with noisy data. Phys. Rev. Lett., 106, 090601
9. Cocco, S., and Monasson, R. (2012). Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests. J. Stat. Phys., 147, 252-314
10. Cocco, S., et al. (2009). Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods. Proc. Natl. Acad. Sci. U. S. A., 106, 14058-14062
11. Cocco, S., et al. (2013). From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction. PLoS Comput. Biol., 9, e1003176
12. Dunn, S.D., et al. (2008). Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics, 24, 333-340
13. Ekeberg, M., et al. (2013). Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. Phys. Rev. E, 87, 012707
14. Ekeberg, M., et al. (2014). Fast pseudolikelihood maximization for directcoupling analysis of protein structure from many homologous amino-Acid sequences. J. Comput. Phys., 276, 341-356
15. Feinauer, C., et al. (2014). Improving contact prediction along three dimensions. PLoS Comput. Biol., 10, e1003847
16. Ferguson, A.L., et al. (2013). Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design. Immunity, 38, 606-617
17. Figliuzzi, M., et al. (2016). Coevolutionary landscape inference and the contextdependence ofmutations in beta-lactamase TEM-1. Mol. Biol. Evol., 33, 268-280
18. Freed, E.O. (2015). HIV-1 assembly, release and maturation. Nat. Rev. Microbiol., 13, 484-496
19. Friedman, N. (2004). Inferring cellular networks using probabilistic graphical models. Science, 303, 799-805
20. Gelman, A., et al. (2008). A weakly informative default prior distribution for logistic and other regression models. Ann. Appl. Stat., 2, 1360-1383
21. Gori, G., and Trombettoni, A. (2011). The inverse ising problem for onedimensional chains with arbitrary finite-range couplings. J. Stat. Mech.: Theory Exp., 2011, P10021
22. Hebb, D.O. (1949). The Organization of Behavior: A Neurophysiological Approach. John Wiley&Sons, New York
23. Hopf, T.A., et al. (2012). Three-dimensional structures of membrane proteins from genomic sequencing. Cell, 149, 1607-1621
24. Jacquin, H., et al. (2016). Benchmarking inverse statistical approaches for protein structure and design with exactly solvable models. PLoS Comput. Biol, 12, e1004889
25. Jaynes, E.T. (1982). On the rationale of maximum-entropy methods. Proc. IEEE, 70, 939-952
26. Kappen, H.J., and Rodr-iguez, F.B. (1998). Efficient learning in Boltzmann machines using linear response theory. Neural Comput., 10, 1137-1156
27. Mann, J.K., et al. (2014). The fitness landscape of HIV-1 Gag: advanced modeling approaches and validation of model predictions by in vitro testing. PLoS Comput. Biol., 10, e1003776
28. Marks, D.S., et al. (2011). Protein 3D structure computed from evolutionary sequence variation. PLoS One, 6, e28766
29. Mastromatteo, I. (2013). Beyond inverse Ising model: structure of the analytical solution. J. Stat. Phys., 150, 658-670
30. Morcos, F., et al. (2011). Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc. Natl. Acad. Sci. U. S. A., 108, E1293-E1301
31. Nguyen, H.C., and Berg, J. (2012). Bethe-Peierls approximation and the inverse Ising problem. J. Stat. Mech.: Theory Exp., 2012, P03004
32. Peyrache, A., et al. (2009). Replay of rule-learning related neural patterns in the prefrontal cortex during sleep. Nat. Neurosci., 12, 919-926
33. Ravikumar, P., et al. (2010). High-dimensional Ising model selection using l1-regularized logistic regression. Ann. Stat., 38, 1287-1319
34. Riedmiller, M., and Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The rprop algorithm. In: IEEE International Conference on Neural Networks, 1993, IEEE, pp. 586-591
35. Roudi, Y., et al. (2009). Ising model for neural data: Model quality and approximate methods for extracting functional connectivity. Phys. Rev. E, 79, 051915
36. Schneidman, E., et al. (2006). Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440, 1007-1012
37. Sessak, V., and Monasson, R. (2009). Small-correlation expansions for the inverse Ising problem. J. Phys. A: Math. Theor., 42, 055001
38. Shakhnovich, E., and Gutin, A. (1990). Enumeration of all compact conformations of copolymers with random sequence of links. J. Chem. Phys., 93, 5967-5971
39. Shannon, C.E. (1948). A mathematical theory of communication. Bell Syst. Tech. J., 27, 379-423
40. Shekhar, K., et al. (2013). Spinmodels inferred frompatient-derived viral sequence data faithfully describe HIV fitness landscapes. Phys. Rev. E, 88, 062705
41. Sulkowska, J.I., et al. (2012). Genomics-Aided structure prediction. Proc. Natl. Acad. Sci. U. S. A., 109, 10340-10345
42. Sutto, L., et al. (2015). From residue coevolution to protein conformational ensembles and functional dynamics. Proc. Natl. Acad. Sci., 112, 201508584
43. Tavoni, G., et al. (2015). Inferred model of the prefrontal cortex activity unveils cell assemblies and memory replay. bioRxiv 10.1101/028316