# StAR: a simple tool for the statistical comparison of ROC curves.

**ABSTRACT** As in many different areas of science and technology, most important problems in bioinformatics rely on the proper development and assessment of binary classifiers. A generalized assessment of the performance of binary classifiers is typically carried out through the analysis of their receiver operating characteristic (ROC) curves. The area under the ROC curve (AUC) constitutes a popular indicator of the performance of a binary classifier. However, the assessment of the statistical significance of the difference between any two classifiers based on this measure is not a straightforward task, since not many freely available tools exist. Most existing software is either not free, difficult to use or not easy to automate when a comparative assessment of the performance of many binary classifiers is intended. This constitutes the typical scenario for the optimization of parameters when developing new classifiers and also for their performance validation through the comparison to previous art.

In this work we describe and release new software to assess the statistical significance of the observed difference between the AUCs of any two classifiers for a common task estimated from paired data or unpaired balanced data. The software is able to perform a pairwise comparison of many classifiers in a single run, without requiring any expert or advanced knowledge to use it. The software relies on a non-parametric test for the difference of the AUCs that accounts for the correlation of the ROC curves. The results are displayed graphically and can be easily customized by the user. A human-readable report is generated and the complete data resulting from the analysis are also available for download, which can be used for further analysis with other software. The software is released as a web server that can be used in any client platform and also as a standalone application for the Linux operating system.

A new software for the statistical comparison of ROC curves is released here as a web server and also as standalone software for the LINUX operating system.

**0**Bookmarks

**·**

**226**Views

- Sigita Glaveckaite, Nomeda Valeviciene, Darius Palionis, Roma Puronaite, Pranas Serpytis, Aleksandras Laucevicius[Show abstract] [Hide abstract]

**ABSTRACT:**This study sought to evaluate the relation between long-term segmental and global functional outcome after revascularisation in patients with chronic ischaemic left ventricular dysfunction (LVD) and baseline markers of viability: late gadolinium enhancement (LGE) transmurality and contractile reserve (CR).Journal of Cardiovascular Magnetic Resonance 10/2014; 16(1):83. · 4.44 Impact Factor -
##### Conference Paper: Classification of atorvastatin effect based on shape and texture features in ultrasound images

[Show abstract] [Hide abstract]

**ABSTRACT:**Carotid atherosclerosis is the major cause of ischemic stroke, a leading cause of mortality and disability. Many research studies have been carried out on how to quantitatively evaluate local arterial effects of potential carotid disease treatments. In this paper, the atorvastatin effect evaluation on atherosclerosis plaques are classified based on various shape and texture features extracted from ultrasound images. First, images of atherosclerotic lesions were extracted manually from ultrasound images by an expert physician. After analysis, 26 shape and 85 texture characteristics, and vessel wall volume (VWV) percent of change, were extracted and calculated from atherosclerotic lesions. Among these, to make the method convenient and exact enough, effective features and VWV percent of change, were selected for drug treatment effect evaluation by physician. Finally, a support vector machine (SVM) classifier was utilized to classify atherosclerosis plaques between atorvastatin group and placebo group. The leave-one-case-out protocol was utilized on a database of 768 carotid ultrasound images of 12 patients (5 subjects of placebo group and 7 subjects of atorvastatin group) for evaluation. The classification results showed overall accuracy 91.67%, sensitivity 95.56%, specificity 86.16%; positive predictive value 90.72%, negative predictive value 93.20%, Matthew’s correlation coefficient 82.81%, Youden’s index 81.72%. And the receiver operating characteristic (ROC) curve in our test also performed well. The experimental results also demonstrate that classification using the combined features has higher accuracy than that only using shape/texture feature or VWV percent of change. The proposed method can be used for the statins effect evaluation, especially when patients are treated with drugs, and further be developed as a beneficial tool for facilitating a physician’s diagnosis of the atherosclerosis.SPIE Medical Imaging; 03/2013 - SourceAvailable from: Paolo ManuntaMarco Simonini, Chiara Lanzani, Elena Bignami, Nunzia Casamassima, Elena Frati, Roberta Meroni, Elisabetta Messaggio, Ottavio Alfieri, John Hamlyn, Simon C Body, C David Collard, Alberto Zangrillo, Paolo Manunta[Show abstract] [Hide abstract]

**ABSTRACT:**Acute kidney injury (AKI) is an important complication of cardiac surgery. Recently, elevated levels of endogenous ouabain (EO), an adrenal stress hormone with haemodynamic and renal effects, have been associated with worse renal outcome after cardiac surgery. Our aim was to develop and evaluate a new risk model of AKI using simple preoperative clinical parameters and to investigate the utility of EO.Nephrology Dialysis Transplantation 06/2014; 29(9). · 3.49 Impact Factor

Page 1

BioMed Central

Page 1 of 5

(page number not for citation purposes)

BMC Bioinformatics

Open Access

Software

StAR: a simple tool for the statistical comparison of ROC curves

Ismael A Vergara†, Tomás Norambuena†, Evandro Ferrada, Alex W Slater and

Francisco Melo*

Address: Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile,

Alameda 340, Santiago, Chile

Email: Ismael A Vergara - ivergara@gmail.com; Tomás Norambuena - tanoramb@gmail.com; Evandro Ferrada - evandro.ferrada@gmail.com;

Alex W Slater - awslater@gmail.com; Francisco Melo* - fmelo@bio.puc.cl

* Corresponding author †Equal contributors

Abstract

Background: As in many different areas of science and technology, most important problems in

bioinformatics rely on the proper development and assessment of binary classifiers. A generalized

assessment of the performance of binary classifiers is typically carried out through the analysis of

their receiver operating characteristic (ROC) curves. The area under the ROC curve (AUC)

constitutes a popular indicator of the performance of a binary classifier. However, the assessment

of the statistical significance of the difference between any two classifiers based on this measure is

not a straightforward task, since not many freely available tools exist. Most existing software is

either not free, difficult to use or not easy to automate when a comparative assessment of the

performance of many binary classifiers is intended. This constitutes the typical scenario for the

optimization of parameters when developing new classifiers and also for their performance

validation through the comparison to previous art.

Results: In this work we describe and release new software to assess the statistical significance of

the observed difference between the AUCs of any two classifiers for a common task estimated

from paired data or unpaired balanced data. The software is able to perform a pairwise comparison

of many classifiers in a single run, without requiring any expert or advanced knowledge to use it.

The software relies on a non-parametric test for the difference of the AUCs that accounts for the

correlation of the ROC curves. The results are displayed graphically and can be easily customized

by the user. A human-readable report is generated and the complete data resulting from the

analysis are also available for download, which can be used for further analysis with other software.

The software is released as a web server that can be used in any client platform and also as a

standalone application for the Linux operating system.

Conclusion: A new software for the statistical comparison of ROC curves is released here as a

web server and also as standalone software for the LINUX operating system.

Background

The prediction of discrete states or categories for any event

or for any object requires a classification process. In order

to be useful, many real-world applications rely on an opti-

mized classification process. These include some impor-

tant problems such as diagnosis of diseases, definition of

Published: 5 June 2008

BMC Bioinformatics 2008, 9:265doi:10.1186/1471-2105-9-265

Received: 1 October 2007

Accepted: 5 June 2008

This article is available from: http://www.biomedcentral.com/1471-2105/9/265

© 2008 Vergara et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Page 2

BMC Bioinformatics 2008, 9:265http://www.biomedcentral.com/1471-2105/9/265

Page 2 of 5

(page number not for citation purposes)

medical treatments, economical and security risk assess-

ment, weather forecast, air traffic regulation and quality

control of industrial processes [1].

Classification tasks in bioinformatics are also common

and can be found in many different and relevant applica-

tions, such as the prediction of genome and protein struc-

ture [2,3], the prediction of the cellular location [4], the

prediction of molecular function [5] and the prediction of

molecular interactions [6]. In general, a classification

process is always involved in the prediction of a pattern

that can be related to some response in living systems.

A popular approach for assessing binary classifiers is anal-

ysis of their ROC curves on a set of representative data

[7,8]. A ROC curve corresponds to a bidimensional plot of

the sensitivity versus 1-specificity for a given classifier with

continuous or ordinal output score. Two main factors

have to be considered by the user when estimating the

ROC curves: 1) The design of the study. Three types of

dataset can be generated when pursuing a classification

task: (i) paired data, where all classifiers are applied to

each individual, (ii) unpaired data, where only one of the

classifiers is applied to each individual, and (iii) partially-

paired data [9], where the dataset is composed of both

paired and unpaired data. In the case of paired and par-

tially-paired datasets, correlation between ROC curves has

to be taken into consideration. Our software is primarily

designed for paired data. However, it can also analyze bal-

anced unpaired data where the number of units is the

same for each classifier. It cannot be used with partially-

paired data. 2) The outcome distribution. Depending on

this factor, three types of approaches can be more or less

appropriate for fitting ROC curves and estimating the cor-

responding AUCs: (i) A parametric approach [10,11],

where we assume a parametric distribution for the out-

comes of the positive and negative individuals. (ii) A sem-

iparametric approach, where we assume that discrete

ordinal outcomes correspond to classification of an unob-

served latent decision variable into ordinal categories

defined by unknown cut-points or threshold values, or

that continuous outcomes can be expressed as an

unknown monotonic transformation of the latent distri-

bution [12], with positive and negative individuals having

different latent decision variables. In this case, parametric

distributions (e.g. normal, logistic, log-normal) are

assumed for the latent decision variables. (iii) A non-par-

ametric approach [13,14], where no distributional

assumptions are made about the outcomes for the posi-

tive and negative individuals.

For each type of approach, different methods for estimat-

ing the AUC after the ROC curve is generated have been

described [11,15,16]. The advantages and disadvantages

of using one or another approach under different scenar-

ios have been previously assessed [17,18]. Among the

advantages of using a parametric or semiparametric

method are that these methods generate a smooth ROC

curve, and the assumption of a distribution provides a

natural means by which statistical inference such as

hypothesis testing and confidence intervals can be

achieved. When data deviate from the assumed distribu-

tion (e.g. normal or log-normal) or simply the outcome

distribution for the positive or negative individuals is

uncertain, non-parametric methods for estimating the

ROC curve become a useful and robust alternative. Even

though the ROC curve generated by non-parametric

methods is jagged, that problem has been tackled in a

non-parametric manner by means of kernel density esti-

mation of the empirical distributions in a previous study

[13].

Although many examples in the existing literature about

the development of new classifiers describe the use of

ROC curves and their corresponding AUCs to assess their

performances, the statistical significance of their differ-

ences is often not reported. This is mostly due to the lack

of freely available software that is easy to use or to auto-

mate for the pairwise comparison of many binary classifi-

ers. Albeit there are several software for performing

statistical ROC analysis [19], to the best of our knowledge,

the only free and readily available software for statistical

ROC analysis that assesses the significance of the differ-

ence of the AUC for a pair of classifiers is ROCKIT [20,21].

This software uses maximum likelihood to fit a binormal

ROC curve to the data and the statistical significance of

the differences of a variety of indexes are assessed on the

basis of a bivariate binormal model. In terms of usability,

it has some drawbacks: 1) the input data format is rather

cumbersome; 2) the output file contains many relevant

data embedded in a human-readable text and thus needs

to be parsed for further analysis; 3) the number of classi-

fiers that can be simultaneously assessed is quite limited;

4) additional software is needed for plotting the ROC

curves; 5) in case of errors, the program does not provide

any feedback to the user about the causes of the abnormal

interruption; and 6) it cannot be easily automated when a

fast comparison of several classifiers is required.

In this work we describe new software that is freely avail-

able as a web server tool and also as a standalone applica-

tion for the Linux operating system that allows the

simultaneous pairwise comparison and statistical assess-

ment of many binary classifiers. The approach chosen is

the nonparametric method for comparing AUCs based on

the Mann-Whitney U-statistic for comparing distributions

of values from two samples [14]. It has been shown that

the AUC calculated by the trapezoidal rule is equal to the

Mann-Whitney U-statistic applied to the outcomes for the

negative and positive individuals. Thus, two or more

Page 3

BMC Bioinformatics 2008, 9:265http://www.biomedcentral.com/1471-2105/9/265

Page 3 of 5

(page number not for citation purposes)

AUCs for paired data can be statistically compared by esti-

mating the covariance matrix for the AUCs, based on the

general theory of U-statistics, and then constructing a

large-sample test in the usual way. The implementation of

this method, not freely available until now, provides the

advantages and robustness of using a nonparametric

approach for estimating AUCs in the case of paired data-

sets, accounting for the inherent correlation of this type of

data. One limitation of this method that may be consid-

ered, as stated by DeLong et al. [14], is that the trapezoidal

rule underestimates the true AUC when the variables take

a small number of discrete values. Among the main fea-

tures of our software are: 1) it is based on a non-paramet-

ric approach for the analysis of AUCs [14], 2) it uses a

simple input format, 3) it can plot multiple ROC curves

simultaneously, 4) the output data is compact, simple and

can be exported for further analysis with other statistical

tools; and 5) it generates a human-readable report in PDF

format, which is useful for a fast initial inspection of the

results.

It is worth noting that a freely available computer pro-

gram for Windows, though still in its beta version, is DBM

MRMC 2.1 [22]. This software is an extended version of a

previous package, LABMRMC [23-28], which allows users

to compare AUCs using the jackknife method. DBM

MRMC 2.1 provides, among other functionalities, statisti-

cal analysis of the AUC computed by the trapezoidal

method, which is equivalent to the AUC computed with

the Mann-Whitney U-statistic. The program uses ANOVA

methods together with jackknifing [23,25,26] (instead of

the Delong method used by our program) to assess the

statistical significance of the observed difference between

two classifiers. Even though this software provides a wide

range of alternatives in ROC fitting, measurement of ROC

indexes and assessment of statistical significance for those

indexes, DBM MRMC is still in its beta version at this

moment and has the same drawbacks in terms of usability

found in ROCKIT (these drawbacks are also present in

LABMRMC package), such as the input/output handling,

lack of automated options for fast analysis of many classi-

fiers and lack of straightforward plotting of results.

Software Implementation

The optimal threshold (OT) for each classifier is defined

after the ROC analysis is performed and consists in the

score value that leads to the maximal accuracy of classifi-

cation. The assessment of the statistical significance of the

differences of the AUCs between two classifiers is imple-

mented as previously described [14]. Briefly, suppose that

R tests are applied on the same N individuals, which can

be classified as positive or negative. Suppose that m of

these individuals are actually positive and n are actually

negative (m+n = N), and that positive individuals tend to

have greater values than negative individuals. If we let

{

and be the sets of outcome values on the r-

th test that correspond to the positive and negative indi-

viduals, respectively (i = 1,...,m; j = 1,...,n; 1 ≤ r ≤ R), the

AUC for each classifier is computed with the Mann-Whit-

ney U-statistic for comparing distributions of values from

two samples, as follows:

The theory on generalized U-statistics allows us to obtain

an estimated covariance matrix for two or more AUC esti-

mates of correlated ROC curves; this R × R matrix is com-

puted as follows:

where the (r1, r2)th element of S10 is given by

and similarly

For further explanation on how to get these estimates,

please refer to reference [14]. Accounting for correlation

between ROC curves is a necessary step for paired data

where two classifiers are used on the same subjects. For

balanced unpaired data the off-diagonal elements of S are

set to zero since the AUCs are not correlated. Conse-

quently, the covariance matrix S is used to compute the

following chi-squared statistic for testing if there is a dif-

ference between two or more classifiers:

Here,

contrast matrix (ie. H0: Lθ = 0, where 0 is the zero matrix).

The statistic follows a chi-squared distribution with

rank(L) degrees of freedom under the null hypothesis of

no difference between classifier AUCs. For a pair of classi-

fiers the statistic reduces to

is the vector of AUC estimates and L is a suitable

Xi

r

}

Yj

r

{}

ˆ

,,

;

;

;

θr

i

r

j

r

i

m

j

n

=∑

1

mn

XY X Y

YX

YX

YX

=

()

() =

<

=

>

⎧

⎪⎪

⎪

⎪

⎨

=

∑

1

1

1

2

0

1

ΨΨ

with

⎩ ⎩

S

mS nS

=+

11

10 01

S

mn

XY

n

XY

r r

1

i

r

j

r

r

j

n

i

r

10

1

−

1

11

2

11

1

2

1

[]

=⋅

()−

⎡

⎢

⎢

⎣

⎤

⎥

⎥⋅

⎦

=∑

,

,,

ΨΨ

θ

j j

r

r

j

n

i

m

2

2

11

()−

⎡

⎢

⎢

⎣

⎤

⎥

⎥

⎦

==

∑∑

ˆθ

S

nm

XY

m

XY

r r

1

i

r

j

r

r

i

m

i

r

01

1

−

1

11

2

11

1

2

1

[]

=⋅

()−

⎡

⎢

⎢

⎣

⎤

⎥

⎥

⎦

⋅

=∑

,

,,

ΨΨ

θ

j j

r

r

i

m

j

n

2

2

11

()−

⎡

⎢

⎢

⎣

⎤

⎥

⎥

⎦

==

∑∑

ˆθ

LLSLL

ˆˆ

θθ

()

′

′

[]

−1

ˆθ

Page 4

BMC Bioinformatics 2008, 9:265http://www.biomedcentral.com/1471-2105/9/265

Page 4 of 5

(page number not for citation purposes)

and a (1 - α)100% confidence interval is given by

This particular software implementation and its successful

application have been recently validated by us through

the comparison to other software [29-31]. In these studies

hundreds of classifiers for the prediction of errors in pro-

tein structures were assessed, and the results of statistical

significance obtained with StAR software were consistent

with those from ROCKIT.

Software Description

The input of the software simply consists of two data files

containing the positive and negative subjects, as defined

by the user. It is important to be aware of the definition

used for the negative and positive data, since the meaning

or interpretation of false positives and true positives

reported will depend on this definition. Each input file

must have a multi-column format, where a given column

contains the obtained scores from a specific classifier for

all subjects tested. Additional input parameters that do

not affect the results of the calculations are optional and

include a job name and the possibility of getting the dis-

play of the classifiers sorted by decreasing AUC values,

among others. Detailed on-line help about the required

format for the input files is provided.

An initial summarized report with the results of the calcu-

lations for each classifier is given in a table that contains a

variable number of columns, which correspond to the fol-

lowing in the most extended output case: 1) Sequential

number of each classifier; 2) selection option of each clas-

sifier to perform further analysis (by default, all classifiers

are selected); 3) description name or identification code

of each classifier; 4) AUC of each classifier; 5) a plus sign

('+') indicating if the classifier score has been inverted in

order to force the AUC greater or equal than 0.5, 6) maxi-

mal accuracy of each classifier (ie. obtained at an optimal

classification threshold that is estimated after the ROC

analysis); 7) optimal classification threshold (ie. the score

value that, when used as a classification threshold, leads

to the maximal accuracy); 8) false positive rate obtained at

the optimal classification threshold; 9) true positive rate

obtained at the optimal classification threshold; 10) total

number of negative subjects evaluated; and 11) total

number of positive subjects evaluated. Online description

is provided for each field in this table.

Additionally, six additional actions on the provided data

are available for further analysis. First, the user can plot

the ROC curves for the selected classifiers. Some graphic

display options or changes to the plots are available. Sec-

ond, the estimated covariance matrix and the p-value of

the global test for a difference between any of the classifi-

ers is displayed. Third, the difference of any two classifiers

provided can be assessed at a given significance level,

which by default is set to 0.05, but it can be modified by

the user. Fourth, for each pairwise comparison of the clas-

sifiers, the software reports the confidence intervals at a

given confidence coefficient. Fifth, a human-readable

report in PDF format that summarizes the results of the

analysis can be generated. Finally, several files containing

the detailed results from the analysis performed by the

user at the selected significance level can be downloaded.

These include the ROC plot points for each classifier, the

estimated covariance matrix, a table containing the p-

value and confidence interval of the AUC difference

observed for each pairwise comparison of classifiers with

color coding of the p-value used to indicate if the differ-

ence was significant.

The standalone version of this software is also released for

the Linux operating system. The Linux version offers the

same capabilities of the web server, but without the

graphic display and the interactive options. A detailed

tutorial that describes how to use the software is available

at the server web site.

Availability and Requirements

Project name: StAR: Statistical Comparison of ROC

Curves.

Project homepage: http://protein.bio.puc.cl/star.html

Operating system(s): any (web server version), Linux

(standalone version).

Programming language: C++, PHP, PERL.

Other requirements: none

License: none

Any restrictions to use by non-academics: none

List of abbreviations used

ROC: Receiver operating characteristic; AUC: Area under

the ROC curve; OT: Optimal threshold; PDF: Portable

document format; StAR: Statistical analysis of ROC curves.

Authors' contributions

IAV and TN developed and implemented the core compu-

ter programs. EF and AWS wrote some additional scripts

L

LSL

ˆθ

(

(

)

′

)

2

Lz LSL

ˆ

/

θ

α

±

′

2

Page 5

Publish with BioMed Central and every

scientist can read your work free of charge

"BioMed Central will be the most significant development for

disseminating the results of biomedical research in our lifetime."

Sir Paul Nurse, Cancer Research UK

Your research papers will be:

available free of charge to the entire biomedical community

peer reviewed and published immediately upon acceptance

cited in PubMed and archived on PubMed Central

yours — you keep the copyright

Submit your manuscript here:

http://www.biomedcentral.com/info/publishing_adv.asp

BioMedcentral

BMC Bioinformatics 2008, 9:265http://www.biomedcentral.com/1471-2105/9/265

Page 5 of 5

(page number not for citation purposes)

and tested the software. FM supervised this project and

wrote the manuscript with the help of IAV. All authors

read and approved the final version.

Acknowledgements

We are truly grateful of the helpful comments and suggestions made by the

anonymous reviewers of this manuscript, which in our opinion have sub-

stantially improved its clarity of presentation. This work was funded by

grant 1051112 from FONDECYT.

References

1.Swets JA, Dawes RM, Monahan J: Better decisions through sci-

ence. Sci Am 2000, 283(4):82-87.

2.Usuka J, Brendel V: Gene structure prediction next term by

spliced alignment of genomic DNA with protein sequences:

increased accuracy by differential splice site scoring. Journal

of Molecular Biology 2000, 297:1075-1085.

3. Orengo CA, Jones DT, Thornton JM: Protein superfamilies and

domain superfolds. Nature 1994, 372(6507):631-634.

4.Chou KC, Elrod DW: Protein subcellular location prediction.

Protein Engineering 1999, 12:107-118.

5.Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM,

Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-

Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M,

Rubin GM, Sherlock G: Gene ontology: tool for the unification

of biology. The Gene Ontology Consortium. Nat Genet 2000,

25(1):25-29.

6.Vazquez A, Flammini A, Maritan A, Vespignani A: Global protein

function prediction from protein-protein interaction net-

works. Nature Biotechnology 2003, 21:697-700.

7. Fawcett T: ROC Graphs: Notes and Practical Considerations

for Researchers. Tech Report HPL-2003-4, HP Laboratories 2004.

8.Swets JA: Measuring the accuracy of diagnostic systems. Sci-

ence 1988, 240(4857):1285-1293.

9.Metz CE, Herman BA, Roe CA: Statistical Comparison of Two

ROC-curve Estimates Obtained from Partially-paired Data-

sets. Medical Decision Making 1998, 18(1):110-121.

10.Hanley JA: The use of the binormal model for parametric

ROC analysis of quantitative diagnostic tests. Medical Decision

Making 1988, 8:197-203.

11.Metz CE: Basic Principles of ROC analysis. Semin nucl med 1978,

8:283-298.

12.Metz CE, Herman BA, Shen JH: Maximum likelihood estimation

of receiver operating characteristic (ROC) curves from con-

tinuously-distributed data. Statistics in Medicine 1998,

17(9):1033-1053.

13.Zou KH, Hall WJ, Shapiro DE: Smooth non-parametric receiver

operating characteristic (ROC) curves for continuous diag-

nostic tests. Statistics in Medicine 1996, 16(19):2143-2156.

14.Delong ER, Delong DM, Clarke-Pearson DL: Comparing the

Areas Under Two or More Correlated Receiver Operating

Characteristic Curves: A Nonparametric Approach. Biomet-

rics 1988, 44(3):837-845.

15. Dorfman DD, Alf E: Maximum likelihood estimation of param-

eters of signal detection theory and determination of confi-

dence intervals - rating - method data. Journal of Mathematical

Psychology 1969, 6:487-496.

16.Bamber D: The area above the ordinal dominance graph and

the area below the receiver operating characteristic graph.

Journal of Mathematical Psychology 1975, 12:387-415.

17. Hajian-Tilaki KO, Hanley JA, Joseph L, Collet JP: A Comparison of

Parametric and Nonparametric Approaches to ROC Analy-

sis of Quantitative Diagnostic Tests. Medical Decision Making

1997, 17(1):94-102.

18.Goddard MJ, Hinberg I: Receiver operator characteristic (ROC)

curves and non-normal data: An empirical study. Statistics in

Medicine 1989, 9(3):325-337.

19. Stephan C, Wesseling S, Schink T, Jung K: Comparison of eight

computer programs for receiver-operating characteristic

analysis. Clin Chem 2003, 49(3):433-439.

20.Metz CE: Statistical analysis of ROC data in evaluating diag-

nostic performance. In Multiple regression analysis: applications in the

health sciences (D Herbert and R Myers, eds) New York: American Insti-

tute of Physics ; 1986:365.

Metz CE: A new approach for testing the significance of differ-

ences between ROC curves measured from correlated data.

In Information processing in medical imaging (Ed F Deconinck) Nijhoff,

The Hague. ; 1984:432-445.

DBM MRMC 2.1 [http://perception.radiology.uiowa.edu]

Dorfman DD, Berbaum KS, Metz CE: Receiver operating charac-

teristic rating analysis. Generalization to the population of

readers and patients with the jacknife method. Invest Radiol

1992, 27:723-731.

Dorfman DD, Metz CE: Multi-reader multi-case ROC analysis:

comments on Begg’s commentary. Academic Radiol 1995,

2(Supplement 1):S76.

Hillis SL, Berbaum KS: Montecarlo validation of the Dorfman-

Berbaum-Metz method using normalized pseudovalues and

less data-based model simplification. Academic radiology 2005,

12:1534-1541.

Hillis SL, Obuchowski NA, Schartz KM, Berbaum KS: A comparison

of the Dorfman-Berbaum-Metz and Obuchowski-Rockette

methods for receiver operating characteristic (ROC) data.

Statistics in Medicine 2005, 24:1579-1607.

Roe CA, Metz CE: Dorfman-Berbaum-Metz method for statis-

tical analysis of multireader, multimodality receiver operat-

ing characteristic data: validation with computer simulation.

Academic radiology 1997, 4(4):298-303.

Roe CA, Metz CE: Variance-component modeling in the anal-

ysis of receiver operating characteristic index estimates. Aca-

demic radiology 1997, 4(8):587-600.

Ferrada E, Melo F: Non-bonded terms extrapolated from non-

local knowledge based energy functions improve error

detection in near native protein structure models. Protein Sci-

ence 2007, 16:1410-1421.

Ferrada E, Vergara IA, Melo F: A knowledge-based potential with

an accurate description of local interactions improves dis-

crimination between native and near-native protein confor-

mations. Cell Biochemistry and Biophysics 2007, 49:111-124.

Melo F, Sali A: Fold assessment for comparative protein struc-

ture modeling. Protein Science 2007, 16:2412-2426.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.