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Table 1 Summary of 27 sequence-based methods in our ensemble coevolution system

From: A new ensemble coevolution system for detecting HIV-1 protein coevolution

Methods*

Statistical methodology

Updated

Ref

ASC/APC

Mutual information

2007

[34]

BN

Bayesian network

2007

[35]

CTMP

Continuous-time Markov model, phylogenetic tree

2007

[36]

CoMap

Compensation coefficient, phylogenetic tree

2007

[37]

Complementary

AA complementary matrix, Pearson coefficient

2006

[38]

CMPro

2D recursive neural networks

2012

[39]

DCA

Maximum entropy model

2011

[25,26]

DNcon

Deep network, Bolzmann machines

2012

[40]

GREMLIN

Maximum entropy model

2013

[41]

Interdependency

Entropy, mutual information

2004

[42]

LogR

Bayesian networks, APC

2010

[43]

MI

Mutual information

2012

[44,45]

MIBP

Mutual information, physicochemical properties

2011

[46]

Mutagenetic

Maximum likelihood mixed trees

2005

[10]

NBZPX2

Normal binary, ZRES

2012

[44]

NCPS

Mutual information, sequence similarity

2009

[47]

NNcon

Neural networks

2009

[48]

PCC

Mutual information, Pearson’s coefficients

2010

[18]

PSICOV

Sparse inverse covariance

2012

[49]

PhysicoMI

Mutual information, physicochemical properties

2012

[6]

PhyCMAP

Random forest, integer linear programming

2013

[50]

RCW

Mutual information

2007

[51]

Spidermonkey

MCMC Bayesian network, phylogenetic tree

2008

[52]

SCA

Statistical free energy couplings

2009

[53]

SVMcon

Support vector machine

2006

[54]

ZRES

Mutual information

2009

[55]

  1. *A comprehensive description of the methodology and our experimental settings are provided in section 2 of Additional file 1: Text S1.