Proteomic fingerprints for potential application to early diagnosis of severe acute respiratory syndrome.
ABSTRACT Definitive early-stage diagnosis of severe acute respiratory syndrome (SARS) is important despite the number of laboratory tests that have been developed to complement clinical features and epidemiologic data in case definition. Pathologic changes in response to viral infection might be reflected in proteomic patterns in sera of SARS patients.
We developed a mass spectrometric decision tree classification algorithm using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Serum samples were grouped into acute SARS (n = 74; <7 days after onset of fever) and non-SARS [n = 1067; fever and influenza A (n = 203), pneumonia (n = 176); lung cancer (n = 29); and healthy controls (n = 659)] cohorts. Diluted samples were applied to WCX-2 ProteinChip arrays (Ciphergen), and the bound proteins were assessed on a ProteinChip Reader (Model PBS II). Bioinformatic calculations were performed with Biomarker Wizard software 3.1.1 (Ciphergen).
The discriminatory classifier with a panel of four biomarkers determined in the training set could precisely detect 36 of 37 (sensitivity, 97.3%) acute SARS and 987 of 993 (specificity, 99.4%) non-SARS samples. More importantly, this classifier accurately distinguished acute SARS from fever and influenza with 100% specificity (187 of 187).
This method is suitable for preliminary assessment of SARS and could potentially serve as a useful tool for early diagnosis.
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ABSTRACT: The ultimate goal of proteomics is to characterize the information flow through protein networks. This information can be a cause, or a consequence, of disease processes. Clinical proteomics is an exciting new subdiscipline of proteomics that involves the application of proteomic technologies at the bedside, and cancer, in particular, is a model disease for studying such applications. Here, we describe proteomic technologies that are being developed to detect cancer earlier, to discover the next generation of targets and imaging biomarkers, and finally to tailor the therapy to the patient.dressNature Reviews Drug Discovery 10/2002; 1(9):683-95. · 33.08 Impact Factor
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ABSTRACT: After the initial phase of exponential growth, the rate of confirmed cases fell to less than 20 per day by April 28. Public-health interventions included encouragement to report to hospital rapidly after the onset of clinical01/2003;
- Grant, P.R. and Garson, J.A. and Tedder, R.S. and Chan, P.K.S. and Tam, J.S. and Sung, J.J.Y. (2003) Detection of SARS Coronavirus in plasma by Real-Time RT-PCR. New England Journal of Medicine, 349 (25). pp. 2468-2469. ISSN 00284793.
Proteomic Fingerprints for Potential Application
to Early Diagnosis of Severe Acute
Xixiong Kang,1Yang Xu,2Xiaoyi Wu,3Yong Liang,4Chen Wang,5Junhua Guo,2
Yajie Wang,1Maohua Chen,13Da Wu,3Youchun Wang,7Shengli Bi,8Yan Qiu,9
Peng Lu,10Jing Cheng,11Bai Xiao,6Liangping Hu,15Xing Gao,12Jingzhong Liu,6
Yiping Wang,3Yingzhao Song,3Liqun Zhang,3Fengshuang Suo,1Tongyan Chen,1
Zeyu Huang,1Yunzhuan Zhao,1Hong Lu,1Chunqin Pan,4and Hong Tang14*
Background: Definitive early-stage diagnosis of severe
acute respiratory syndrome (SARS) is important despite
the number of laboratory tests that have been developed
to complement clinical features and epidemiologic data
in case definition. Pathologic changes in response to
viral infection might be reflected in proteomic patterns
in sera of SARS patients.
Methods: We developed a mass spectrometric decision
tree classification algorithm using surface-enhanced la-
ser desorption/ionization time-of-flight mass spectrom-
etry. Serum samples were grouped into acute SARS (n ?
74; <7 days after onset of fever) and non-SARS [n ?
1067; fever and influenza A (n ? 203), pneumonia (n ?
176); lung cancer (n ? 29); and healthy controls (n ?
659)] cohorts. Diluted samples were applied to WCX-2
ProteinChip arrays (Ciphergen), and the bound proteins
were assessed on a ProteinChip Reader (Model PBS II).
Bioinformatic calculations were performed with Bi-
omarker Wizard software 3.1.1 (Ciphergen).
Results: The discriminatory classifier with a panel of
four biomarkers determined in the training set could
precisely detect 36 of 37 (sensitivity, 97.3%) acute SARS
and 987 of 993 (specificity, 99.4%) non-SARS samples.
More importantly, this classifier accurately distin-
guished acute SARS from fever and influenza with
100% specificity (187 of 187).
Conclusions: This method is suitable for preliminary
assessment of SARS and could potentially serve as a
useful tool for early diagnosis.
© 2005 American Association for Clinical Chemistry
Since November 1, 2002, severe acute respiratory syn-
drome (SARS)16has affected 32 countries and regions,
with 8422 reported probable cases, 916 deaths, and local
transmission in at least 6 countries (1). Collective efforts
have been made to identify its epidemiologic determinant
as a novel member of Coronaviridae, SARS-associated
coronavirus (SARS-CoV) (2–6), and etiologic experiments
in cynomolgus macaques have confirmed the virus as the
1Center for Laboratory Diagnosis, Beijing Tiantan Hospital and Capital
University of Medical Sciences, Beijing, China.
2Ciphergen Biosystems, Inc., Beijing, China.
3Deyi Diagnosis Institute, Beijing, China.
4Taizhou Municipal Hospital, Taizhou, Zhejiang Province, China.
5Institute of Respiratory Medicine and6Basic Medical Research Center,
Chaoyang Hospital and Capital University of Medical Science, Beijing, China.
7Department of Cell Biology, National Institute for the Control of Phar-
maceutical and Biological Products (NICPBP), Beijing, China.
8Institute of Virology, Chinese Academy of Preventive Medicine, Beijing,
9Department of Quality Control, Beijing Red Cross Blood Center, Beijing,
10Society of Blood Transfusion, Beijing, China.
11National Engineering Research Center for Beijing Biochip Technology,
Tsinghua University, Beijing, China.
12Beijing Center for Disease Control and Prevention, Beijing Bureau of
Public Health, Beijing, China.
13Department of Neurosurgery, The Affiliated Hospital of Xuzhou Med-
ical College, Jiangsu Province, China.
14Center for Molecular Immunology, Institute of Microbiology, Chinese
Academy of Sciences, Beijing, China.
15Consulting Center of Biomedical Statistics, Academy of Military Medical
Sciences, Beijing, China.
*Address correspondence to this author at: Center for Molecular Immu-
nology, Chinese Academy of Sciences, 13 Zhongguancun Bei Yi Tiao, PO Box
2714, Beijing, China 100080. Fax 86-10-62638849; e-mail firstname.lastname@example.org.
Received February 9, 2004; accepted October 21, 2004.
Previously published online at DOI: 10.1373/clinchem.2004.032458
16Nonstandard abbreviations: SARS, severe acute respiratory syndrome;
CoV, coronavirus; SELDI-TOF MS, surface-enhanced laser desorption/ioniza-
tion time-of-flight mass spectrometry; and PBS, Protein Biological System.
Clinical Chemistry 51:1
causative agent for SARS (7, 8). Rapid progress has also
been made in the determination of its genome sequences
(9–11) and the molecular evolution of the coronavirus
(12). Identification of angiotensin-converting enzyme 2 as
the viral receptor provided further information toward
deciphering its molecular mechanisms of infection (13).
Despite such advances in virologic studies, early diag-
nosis of SARS has been based primarily on the clinical
definitions released by WHO and CDC (14, 15), which can
be confusing or contradictory (16). Available serologic
tests cannot guarantee an early diagnosis (17), and PCR-
based molecular detection of the viral RNA suffers from
unsatisfactory sensitivity and specificity (3, 17–19). In the
last year, failure to develop diagnostic tests for SARS,
especially in the acute phase, severely impacted specific
prevention and treatment measures for SARS. There is a
need to establish a reliable diagnostic methodology for
SARS-CoV, in particular, to distinguish the similar clinical
manifestations of SARS and other respiratory tract infec-
tions. This urgency is reinforced by the first SARS case not
linked to laboratory contamination, which occurred in
Guangdong, China this year (20).
Proteomic analysis has provided a unique tool for the
identification of diagnostic biomarkers, evaluation of dis-
ease progression, and drug development (21, 22). Surface-
enhanced laser desorption/ionization time-of-flight mass
spectrometry (SELDI-TOF MS) enables rapid, reproduc-
ible protein/peptide profiling of multiple disease-specific
biomarkers directly from crude samples (e.g., tissue cell
lysates or body fluids) (23, 24). Small amounts of sample
can be applied directly to a biochip coated with specific
chemical matrices (e.g., hydrophobic, cationic, or anionic)
or specific biochemical materials such as DNA fragments
or purified proteins. The bound proteins/peptides can
then be analyzed by MS to obtain the protein fingerprints,
or even amino acid sequence determinants, when inter-
faced to a mass spectrometric microsequencing device.
Analogous to the proteomic detection of various can-
cers (25, 26), we used a weakly cationic ProteinChip
(WCX2 chip surface) to retrospectively analyze SARS sera
to determine whether there are distinct and reproducible
protein fingerprints potentially applicable to the diagno-
sis of SARS. We established a decision tree algorithm
consisting of four unique biomarkers for acute SARS in
the training set and subsequently validated the accuracy
of this classifier by use of a completely blinded test set.
Materials and Methods
patients and samples
More than 2000 serum specimens from suspected/prob-
able SARS patients admitted to 38 major hospitals in the
Beijing area between April 14 and June 5, 2003, were
eligible for inclusion. The serum procurement, data man-
agement, and blood collection protocols were approved
by the Beijing SARS-Control Working Group and were in
accordance with WHO biosafety guidelines (27). Among
the retrospective samples, only 74 were selected from
probable patients whose blood samples were collected
with onset of fever within 7 days at the time of admission
(acute SARS patients; Table 1). Probable cases were based
on the eligibility criteria set forth by WHO (15). These
cases had also radiographic evidence of infiltrates consis-
tent with pneumonia or respiratory distress syndrome on
chest x-ray. The paired convalescent serum samples from
the SARS cohort tested positive for IgM seroconversion by
the IFA method (Beijing Genomics Institute), and four
samples also tested positive in a DNA array test using
nasopharyngeal samples. The 1067 non-SARS control se-
Table 1. Patients with acute SARS who matched the fit in WHO SARS case definition.
A, C, J, K
A, B, H, N, S, Y
D, E, H, J, K, S, T, X, Y
A, D, E, J, K, M
A, D, H, J, S, T, X, Y
C, E, J, M, O, P, S, T
aCases from April 15 to June 5, 2003, with retrospective serum samples collected ?7 days after self-described onset of symptoms. The ages of these cohorts varied
from 6 to 74 years. Each group of samples was divided into two parts for training and blinded tests.
bAbbreviations for hospitals in Beijing area: A, Civil Aviation Hospital; B, Beijing Center for Disease Control and Prevention; C, Concord Hospital; D, Dongzhimen
Hospital; E, Earth Temple Hospital; H, Chaoyang Hospital; J, Jishuitan Hospital; K, Peking University Medical School 3rd Affiliate Hospital; M, Martial Police General
Hospital; N, North Suburban Hospital; O, Osier Hospital; P, State Power Hospital; S, Shijingshan Hospital; T, Tongren Hospital; X, Jiuxianqiao Hospital; Y, Youan
cIFA, immunofluorescence assays; NA, not available.
dIncluded patients were positive for IgM seroconversion in immunofluorescence assays with the paired convalescent sera. The other information on microbiological
tests, clinical records, or treatment were not accessible because of the classified nature of the work performed by Beijing SARS-Control Working Group.
eFour included patients tested positive in a DNA chip array method (Xiao et al, manuscript in preparation) with four sets of DNA probes derived from SARS-CoV
genome coding replicase 1A (2 independent probes), spike, and nucleocapsid genes. Other patients were negative by real-time fluorescent RT-PCR of nasopharyngeal
Clinical Chemistry 51, No. 1, 2005
rum samples (Table 2) were obtained from recruited
healthy donors (n ? 659) or from patients with respiratory
infections [pneumonia (n ? 176) or high fever (n ? 203; 66
with influenza A)] or lung cancer (n ? 29). The control
samples were all negative for SARS-CoV seroconversion.
The patients and serum samples were then divided
into two groups: one for the “training” set and the other
for the blinded “test” set (Tables 1 and 2). SARS and
non-SARS control sera were all stored at ?80 °C in 30-?L
aliquots. Before each round of mass spectrometric assays,
we routinely performed quality control of serum samples
by the appearance and peak intensity of m/z 6635.09 (Fig.
3A). Because the peak intensity of m/z 6635.09 remained
relatively constant among spectra from different assays
and different instruments, it was also used for normaliza-
tion between each round of analyses.
Three different chip chemistries (hydrophobic, anionic,
and cationic) were first evaluated to determine which
affinity chemistry gave the best serum profiles in terms of
the number and resolution of proteins. The weakly cat-
ionic exchange chip (WCX) gave the best results with
mass spectra from 0 to 200 kDa. The WCX chips in an
8-well bioprocessor format (Ciphergen) were chosen to
allow a larger volume of serum for the chip array. The
bioprocessor was pretreated with 150 ?L of 100 mmol/L
sodium acetate (pH 4) on a platform shaker at 250 rpm for
5 min. The excess sodium acetate was removed by invert-
ing the bioprocessor on a paper towel. This process was
repeated twice. The serum samples were thawed on ice in
a Biosafety Level II cabinet, and 20 ?L of each sample was
mixed with 30 ?L of U9 buffer (9 mol/L urea, 10 g/L
CHAPS in phosphate-buffered saline) in a 1.5-mL Eppen-
dorf tube and vortex-mixed at 4 °C for 20 min. We then
added 100 ?L of U1 buffer [U9 buffer diluted by ninefold
(100 mL of U9 buffer plus 800 mL of Tris-HCl) with 50
mmol/L Tris-HCl (pH 7)] to the serum/urea mixture,
vortex-mixed it for 10 min, and stopped the reaction by
addition of 600 ?L of sodium acetate on ice. We applied 50
?L of the serum/urea sample to each well, and the
bioprocessor was sealed and shaken on a platform shaker
at 250 rpm for 30 min. The excess serum/urea solution
was discarded, and the bioprocessor was washed three
times with 100 mmol/L sodium acetate as described
above. The chips were removed from the bioprocessor,
washed twice with deionized water, and air-dried. Sub-
sequently 0.5 ?L of EAM sinapinic acid saturated in 500
mL/L acetonitrile–5 g/L trifluoroacetic acid was added to
each well. After air-drying, the sinapinic acid application
Chips were then placed in the Protein Biological Sys-
tem II (PBS II) mass spectrometer reader (Ciphergen), and
TOF spectra were generated by an average of 104 laser
shots collected in the positive mode. The settings for
low-energy readings were set with a high mass of 50 kDa
and were optimized from 3 to 15 kDa at a laser intensity
of 200, detector sensitivity of 8, and a focus by optimiza-
tion center. High-energy readings were set with a high
mass of 200 kDa and were optimized from 10 to 50 kDa at
a laser intensity of 230 and a detector sensitivity of 9. Mass
accuracy was calibrated externally by use of the All-in-
One peptide molecular mass calibrator (Ciphergen).
Sera from a healthy control were individually applied
to seven bait surfaces of eight WCX2 chips and run during
3-day intervals for analysis of within-run reproducibility.
In parallel, 40 samples (10 from SARS patients, 10 from
patients with fever, 10 from patients with pneumonia, and
10 from health controls) were applied in duplicate to a
single chip and run on two different instruments (PBS II
and PBS IIc; Ciphergen) for between-run analysis of
instrument drift. To avoid the possibility that placement
or run order of samples would affect assay accuracy,
samples were loaded on chips in a rotational fashion. In
Table 2. Control cohorts with various respiratory inflammations and carcinomas.
Patients, n (M/F)
38.7–40.1 °C; Fluc(n ? 66)
CXR, P (n ? 75); MP (n ? 57); P?TB (n ? 44)
CXR ? pathology (n ? 3); CT (n ? 16)
aSera from healthy persons attending Anzhen Hospital (n ? 14) were collected in 2001, sera from 307 Hospital (n ? 10) were collected before November 2002,
and sera from Deyi Diagnostic Institute (n ? 21; Beijing; epidemic region) and Taizhou Hospital (n ? 34; Zhejiang Province; nonepidemic region) were collected on
June 3, 2003. The rest of the healthy control sera, from Beijing Red Cross Blood Center, were collected between July and December 2003.
bSerum samples from patients with high fevers were collected from Taizhou Hospital, Zehjiang Province (nonepidemic region), on June 3, 2003; from Chaoyang
Hospital on November 15, 2003; and from Di Tan Hospital on November 22 and December 3, 2003. Among them, 66 were positive in the influenza A IgM ELISA.
cFlu, influenza; CXR, chest x-ray; MP, mycoplasma; P, pneumonia; TB, mycobacterium tuberculosis; CT, computed tomography.
dSerum samples were collected from Tiantan Hospital (n ? 12), Beijing, on May 3, 2003; from Taizhou Hospital (n ? 54), Zehjiang Province, on June 3, 2003; from
Chaoyang Hospital (n ? 38) on November 25, 2003; and from Ditan Hospital (n ? 72) on December 3, 2003. All patients had positive chest x-rays and manifested
with pneumonia or atypical pneumonia; 57 tested positive in the mycoplasma IgM ELISA, and 44 were positive in both the pneumonia and tuberculosis PCR assays.
eDiagnosis was based on the criteria in Surgery, 5th edition (Zaide Wu. Beijing, China: Public Health Press). Clinical features included various forms of metastasis
in the pericardium (n ? 1), upper right clavicle (n ? 1), lymph nodes (n ? 1), liver (n ? 1), and brain (n ? 1); accompanying hydrothorax was also observed in nine
Kang et al.: Early Diagnosis of SARS Using Proteomic Fingerprints
brief, sample 1 was spotted on the 8-well directional chip
(wells A to H) in duplicate in wells A and B and then in
wells G and H of the second chip. Samples 2, 3, and 4 were
loaded on chips in the same rotation order. We also
randomized the order of chip placement in the spectrom-
eter to minimize bias from run order. Spectra were
collected for each sample and analyzed independently
using the classification algorithm established in the train-
The peak at m/z 6635.09 in the quality-control serum
was adjusted to have an intensity of 40–60 for both the
PBS II and PBS IIc. The peak intensity of m/z 6635.09 in the
quality-control serum was used to normalize instrument
resolution between the PBS II and PBS IIc. We normalized
spectra using total ion current with an identical normal-
ization coefficient and a low mass cutoff ?2000 Da. If the
factor was ?0.3 or ?2.9 after normalization to total ion
current for the peak at m/z 3939, repeated runs would be
performed. No outlier was rejected in the test. The “root”
biomarker, m/z 3939, yielded the lowest and similar P
value in both the PBS II and PBS IIc.
bioinformatics and biostatistics
Peak detection was performed with Biomarker Wizard
software 3.1.1 (Ciphergen). The m/z ratios between 2000
and 20 000 were selected for analysis because this range
contained the majority of the resolved protein and pep-
tides. The m/z range between 0 and 2000 was eliminated
from analysis to avoid interference from adducts, artifacts
of the energy-absorbing molecules, and other possible
chemical contaminants. Peak detection involved baseline
subtraction, mass normalization using a common cali-
brant peak (m/z 6635.09), and normalization to the total
ion current intensity with a minimum m/z of 2000, using
an external normalization coefficient of 0.2 (normalization
factor for individual spectrum ? 0.2/average ion current
for each spectrum) for spectra obtained at different times
or locations. The settings used for autodetect peaks to
cluster in the first pass were a signal-to-noise ratio of 5
and a minimum peak threshold of 5% of all spectra. The
peak clusters were completed by second-pass peak detec-
tion using a signal-to-noise ratio of 2 and 0.3% of mass for
the cluster window. An average of 99 peaks was detected
in each spectrum. The mass range from 20 to 200 kDa was
analyzed in parallel.
Data analysis.The data analysis process used in this study
involved three stages: (a) peak detection and alignment;
(b) selection of peaks with the highest discriminatory
power; and (c) data analysis using a decision tree algo-
rithm. A random sampling (acute SARS, fever, pneumo-
nia, lung cancer, and healthy) with two strata (acute SARS
and non-SARS) was used to separate the entire data set
into training and test data sets. The training data set
consisted of SELDI spectra from 37 acute SARS and 74
non-SARS serum samples. The validity and accuracy of
the classification algorithm were then challenged with a
blinded test data set consisting of 37 acute SARS and 993
Decision tree classification. Construction of the decision tree
classification algorithm was performed as described pre-
viously (26) with modifications based on the Biomarker
Patterns Software (Ciphergen). Classification trees were
split into two branches or nodes, using one rule at a time.
We set target the variable level at 2 and the minimum
value at 0, and the decision was made based on the
presence or absence and the intensity of one peak, using
the Gini or Twoing method, favoring even splits from 0.00
to 2.00 and varied by 0.2 each time, and with V-fold
cross-validation from 6 to 12 changed by 2 for the growth
of 88 trees. The lowest cost tree (value ? 0.068; Gini ? 2.0;
V-fold ? 10) was selected for the final test.
tree classification and pattern discovery
To identify the serum biomarkers that could distinguish
SARS from non-SARS samples, we used a training set of
specimens (37 SARS acute and 74 controls; Tables 1 and 2)
and constructed the decision tree classification algorithm
using 10 989 peaks [99 peaks ? (37 ? 74) spectra] of
statistical significance identified in the low energy read-
ings (see Materials and Methods). The classification algo-
rithm used four peaks between 3 and 12 kDa (m/z 3939.08,
4137.71, 8136.64, and 11 514.2) and generated five terminal
nodes (Fig. 1). These discriminatory peaks efficiently split
SARS specimens into terminal nodes 3 and 5 and non-
SARS samples into terminal nodes 1, 2, and 4. Each mass
peak showed a mean intensity ratio of SARS vs non-SARS
?3 and a P value close to 0 (Table 3). Notably, the protein
or peptide with masses at 3939.08, 8136.64, and 11 514.2
Da was up-regulated in patients with acute SARS,
whereas that of a mass at 4137.71 Da was down-regulated
compared with healthy controls or patients with respira-
tory tract infections. A representative spectrum of a SARS
specimen aligned with that of a healthy control (Fig. 2A)
showed the four fingerprints in node 3 required for
pattern recognition in the classifier. The unique presence
of the root biomarker, m/z 3939.08, is demonstrated in the
alignment of representative spectra of samples from pa-
tients with acute SARS (1, 3, 5, and 7 days after the onset
of fever; from terminal node 5) and those from healthy
controls and patients with fever and influenza or pneu-
monia (Fig. 2B). This decision algorithm correctly classi-
fied 37 of 37 (100%) of the acute SARS samples and 72 of
74 (97.3%) of the non-SARS controls in the training set
The above classifier used only those masses in the
low-energy readings (m/z ?50 000). To exhaust all mean-
ingful serum biomarkers, we expanded the analysis of the
same training samples in the high-energy setting (m/z
Clinical Chemistry 51, No. 1, 2005
?200 kDa, see Materials and Methods) and pooled both
low- and high-energy readings together [161 ? (37 ?
74) ? 17 871 peaks]. The classification algorithm then
used five peaks between 4 and 16 kDa (m/z 4824.28,
8136.64, 11505.30, 14 023.00, and 15 369.20; peaks at m/z
8136.64 and 11 505.30 overlapped with those in Fig. 1) in
six terminal nodes and yielded a sensitivity and specific-
ity of 94.6% (35 of 37) and 95.9% (71 of 74), respectively
(data not shown). The peaks at m/z 3939.08 and 4137.71 in
this new classifier disappeared because their correspond-
ing peak intensities were beyond the limits after normal-
ization with the intensity for the peak at m/z 6635.09 (see
the section on patients and samples in the Materials and
Methods). However, because most of the SARS cases in
this alternative classifier (34 of 37) fell into the terminal
node where the proteins/peptides were down-regulated
(m/z 14023.0 ?0.611087, m/z 4824.28 ?0.746989, and m/z
15369.2 ?3.27656), and because this algorithm had to
Fig. 1. Diagram of the decision tree classification in the training data set.
The numbers in the root node (top), the descendant nodes (ovals), and the terminal nodes 1–5 (rectangles) represent the classes. S, SARS; NS, non-SARS; N, sum
of S and NS. The numbers below the root and descendant nodes are the mass values followed by the peak intensity values. For example, the mass value under the
root node is 3939.08 kDa, and the intensity is ?1.7107.
Table 3. Biomarker statistics for SARS vs non-SARS spectra and decision tree classification.a
3 ? 10?10
a,bThe 95% confidence intervals were estimated using the principle of binominal distribution:afor sensitivity, the 95% confidence interval was 90.5–100.0% for the
training set and 85.8–99.9% for the test set;bfor specificity, the 95% confidence interval was 90.6–99.7% for the training set and 91.9–96.9% for the test set.
Kang et al.: Early Diagnosis of SARS Using Proteomic Fingerprints