Original Article
Data Mining in Proteomic Mass Spectrometry
Asha Thomas,
Georgia D.Tourassi,
Adel S. Elmaghraby,
Roland Valdes Jr.,
and Saeed A. Jortani,
Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY;
Digital Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center,
Durham, NC; and
Department of Pathology and Laboratory Medicine, University of Louisville,
Louisville, KY
*Author to whom all correspondence and reprint requests should be addressed:
Saeed A. Jortani, Department of Pathology, University of Louisville, Louisville, KY.
E-mail: sjortani@louisville.edu.
Clinical Proteomics
Copyright © 2006 Humana Press Inc.
All rights of any nature whatsoever are reserved.
ISSN 1542-6416/06/02:13–32/$30.00 (Online)
Data mining application to proteomic data
from mass spectrometry has gained much
interest in recent years. Advances made in pro-
teomics and mass spectrometry have resulted
in considerable amount of data that cannot be
easily visualized or interpreted. Mass spectral
proteomic datasets are typically high dimen-
sional but with small sample size. Conse-
quently, advanced artificial intelligence and
machine learning algorithms are increasingly
being used for knowledge discovery from such
datasets. Their overall goal is to extract useful
information that leads to the identification of
protein biomarker candidates. Such biomark-
ers could potentially have diagnostic value as
tools for early detection, diagnosis, and prog-
nosis of many diseases. The purpose of this
review is to focus on the current trends in
mining mass spectral proteomic data. Special
emphasis is placed on the critical steps
involved in the analysis of surface-enhanced
laser desorption/ionization mass spectrometry
proteomic data. Examples are drawn from pre-
viously published studies and relevant data
mining terminology and techniques are
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14 _______________________________________________________________________________ Thomas et al.
Key Words: Mass spectrometry; data mining;
artificial intelligence; data analysis; SELDI; linear
discriminant analysis; artificial neural networks.
As the technology involved in biological
domains like proteomics advances, large vol-
umes of data are continuously generated.
However, the data analysis required is
increasingly overwhelming to the scientists
in these domains. Contrarily, computational
scientists are accustomed to dealing with
large databases from domains such as mar-
keting, financial institutions, and telecommun-
ication. They typically face the challenge by
the use of powerful machine learning and
data mining tools. Such tools are the culmina-
tion of continuous advances made in the areas
of artificial intelligence, statistics, and database
management. Therefore, it is natural that the
proteomics and computational intelligence
domains find their paths crossed with the
common goal of extracting clinically useful
information from the wealth of proteomic
spectral data.
The purpose of this review is to address
some of the key issues involved in the appli-
cation of data mining approaches to proteomic
data from surface-enhanced laser desorption/
ionization mass spectrometry (SELDI-MS). In
addition, terminologies and popular strategies
in mining of mass spectral proteomic data are
described. The article is organized as follows.
First, a brief introduction to data mining and
proteomics is provided. Then, the need for
applying data mining to proteomic data from
SELDI-MS is explained. Thereafter, important
steps in the mining process are outlined and
explained with examples drawn from recent
studies involving SELDI-MS data.
Data Mining in Proteomics
Proteomics is an emerging area in bioinfor-
matics. First coined by Australian scientist
Marc Wilkins in 1994 (1), proteomics is a sci-
ence that deals with the study of proteins and
their interactions in an organism. The major
focus of most proteomic studies is the dis-
covery of the proteins of interest known as
biomarkers (2). The discovery of biomarkers
is potentially valuable for the early detection,
diagnosis, and monitoring of diseases (e.g.,
refs. 3–6). Active and ongoing biomedical
research advances coupled with the discov-
ery of novel and powerful diagnostic pro-
teomic tools have further strengthened the
progress made.
There are several techniques for protein or
peptide profiling using mass spectroscopy.
Two of the major techniques intended for pro-
teomic mass spectrometry are matrix-assisted
laser desorption/ionization (MALDI-MS) (7a)
and its extension, the SELDI-MS (7–8). In both
techniques, the biological matrix is mixed with
the energy absorbing matrix and following
shinning of the laser energy in vacuum envi-
ronment, which is absorbed by the proteins
causing them to get ionized. After application
of an electric field, the ions accelerate through
a flight tube until finally being detected by the
instrument. The major difference between
MALDI and SELDI is the fact that the latter
uses additional chemistries on the surface of
the chips to further isolate the proteins
intended to be analyzed from the other
molecules or proteins in the matrix (7). This
review will focus mainly on issues related to
the data mining of SELDI proteomic data
because many of the recent discoveries of
potential new biomarkers have involved this
Typically, protein profiles in body fluids
such as serum, urine, or nipple aspirate, and
in some studies tumor tissue, are analyzed
using SELDI mass spectrometry (Ciphergen
Biosystems, Fremont, CA). The output is a
protein expression profile that is often a dataset
containing information of protein peaks and
their heights (intensities) in one or more
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Data Mining in Proteomic MS ________________________________________________________________ 15
groups of patients (e.g., cancer, benign,
healthy). The main goal of proteomic studies
using mass spectral data is to identify protein
patterns that are capable of reliably differentia-
ting between various groups under study. The
identification of these protein biomarker pat-
terns that relate to a certain pathological state
may aid in the early detection and prognosis
of that disease.
However, identification of these biomarker
patterns from mass spectral data is a challeng-
ing task. Issues such as few samples and large
number of peaks, typical in MS data, have to
be handled by advanced analytical techniques.
Data Mining
In several studies, protein profiles gener-
ated from SELDI-MS have been analyzed
using statistical and data mining methods.
Data mining is an approach used in scientific
and business domains to extract meaningful
and useful information from large and com-
plex sets of data. The process of data mining
is typically iterative (9) wherein previously
unknown and potentially useful information
is discovered by the use of powerful analyt-
ical techniques. The discovery process
involves finding relationships and patterns in
raw data that can be either utilized or assessed
by decision makers and analysts. Steps
involved in mining include data preparation,
feature selection, model development or pat-
tern recognition, and model assessment fol-
lowed by application of developed model to
new cases.
The early use of data mining was in finan-
cial institutions and marketing (10–13). How-
ever, with an explosion in the amount of data
being stored electronically, data mining has
reached across telecommunications (14), retail
(15), manufacturing (16), health care (17),
fraud detection (18), homeland security (19),
and biomedical domains (20).
Data mining methods have evolved from
advancements made in artificial intelligence,
statistics, and database management. There
are various data mining algorithms that are
available today, based on different theoretical
concepts. Some are in the form of decision
trees (DTs), known for their ease of inter-
pretability. Others, like artificial neural net-
works (ANNs), capture complex and nonlinear
relationships in the data. ANNs are considered
very powerful but are less interpretable. The
advantages and disadvantages of each method
when applied to SELDI-MS data are discussed
in the following sections.
Application of Data Mining in SELDI-MS
Proteomic Data
As previously mentioned, the use of mass
spectrometry in proteomics results in a large
number of high-dimensional data, where the
number of features (peaks) is much greater
than the number of samples. Data samples
from SELDI-MS are typically comprised of
hundreds to thousands of protein peaks. Such
a vast number of data cannot be visually ana-
lyzed or handled by ordinary data mining
tools. Tackling such a high-dimensional struc-
ture of a comparatively small-sized data set is
a significantly challenging task. An example of
two typical spectra for serum samples is
depicted in Fig. 1.
In their quest for adequate tools for analyz-
ing the data in hand and retrieving useful
information, scientists in proteomics are
increasingly dependent on advanced data
mining techniques that can tackle issues such
as the curse of dimensionality and limited
data sets. These advanced techniques include
artificial intelligence and machine learning.
Current practices in mining protein mass
spectrometry data from SELDI include the fol-
lowing steps:
1. Data modeling using peaks identified by
preprocessing and feature selection.
2. Careful data sampling to address the small
sample size typical in SELDI-MS data.
3. Performance evaluation of the data models.
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16 _______________________________________________________________________________ Thomas et al.
The previously listed critical steps have to be
carefully addressed by proteomic researchers to
obtain accurate and robust decision models.
The steps are repeated iteratively and modifi-
cations are made so as to probe and explore
different aspects of the data. Figure 2 outlines
the mining process and the possible methods
that are available under each of them. Each
data-processing step is described in more
detail in the following sections.
The raw data obtained from SELDI-MS is
noisy. The goal of preprocessing is to improve
the quality of data for the subsequent steps.
The results of classifier algorithms will be mis-
leading and adversely affected when the qual-
ity of the data is poor. Thus, preprocessing
and preparing the data is crucial in the analy-
sis of raw SELDI-MS proteomic data.
Most of the published studies have used
the software provided by the SELDI manu-
facturer for preprocessing. The SELDI soft-
ware detects the locations and intensities of
the proteins in each of the samples and carries
out important preprocessing steps like base-
line subtraction, intensity normalization, peak
alignment, and peak detection. The criteria
specified by the SELDI operator are used to
filter the peaks.
Fig. 1. Serum mass spectra. The top panel depicts the mass spectrum of a serum sample collected from a
patient with inadequate heart function (test) and the bottom panel shows that of a patient with adequate func-
tion (control). Please note that the m/z for many peaks in both spectra appear to be similar, whereas others
are different between the two patients.These spectra have been obtained using weak cation-exchange surfaces
without prefractionation of serum samples.
Baseline subtraction removes the electronic
and chemical noise. It is typically performed
in two steps. First, the baseline is estimated
using either parametric or nonparametric
methods. Then, the estimated baseline is sub-
tracted from the original mass spectrum. For
example, in the SELDI software available by
Ciphergen, baseline subtraction is performed
by applying a filter window on the mass spec-
trum, estimating the average or minimum
intensity within the window, and then moving
the window across the spectrum to estimate
the overall baseline. The size of the moving
window is a critical parameter that signifi-
cantly changes the outcome of the de-noising
step. To date, there are no studies comparing
the effectiveness of the available baseline
reduction techniques and it appears that
researchers optimize this step in a heuristic
manner that works best with their own data
sets. Although most studies are focused on
reducing the low-frequency noise in the spec-
tra, there have been many attempts to charac-
terize and subtract the high-frequency noise
components as well.
With baseline reduction completed, normal-
ization is the next step. Because a peak in a
spectrum describes only the relative amount of
a protein, normalization is done to ensure
meaningful comparisons across spectra. Once
preprocessing is performed, the peaks obtained
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are analyzed by further dimension reduction
Feature Selection
The Curse of Dimensionality
Most of the problems in data mining are
related to the size and the complexity of the
data sets. In data mining, a data set is referred
to as a file or a table of rows and columns,
where the rows are the records or cases and
columns are the dimensions or features or
attributes of the data. A data set with n dimen-
sions is referred to as n-dimensional. Typical
data mining classifiers are suited to problems
where the number of features is few and the
number of samples is large like the data avail-
able in retailers and manufacturers.
The straightforward approach would be
to use the normalized intensity of every
peak present in the spectrum as a feature.
Unfortunately, in proteomic applications the
number of features (dimensions) is very large
(e.g., 15,000 protein peaks) and the number
of data points is comparatively small (e.g.,
150 patients). The data samples that have
extremely high number of measurable quan-
tities are referred to as high-dimensional data.
If a one-dimensional data space has n samples,
then a k-dimensional data space must have n
different samples to achieve the same density.
However, it is often very difficult to obtain
this level of density in mass spectroscopy
This problem is known in data mining as
the curse of dimensionality. As the dimension-
ality of the input data space (i.e., the number
of features) increases, it becomes exponentially
more difficult to fit robust decision models (9).
It is seen that the data often contains noisy
features with very little or no information
value and therefore result in the development
of poor and misleading classifiers. Hence, it is
simply a practical necessity to prescreen from
among a large set of input variables those that
are of likely utility for predicting the outputs
of interest.
Prescreening the spectra to identify peaks
(or features) is known as feature extraction.
Feature extraction is typically done by bin-
ning. According to this process, each group of
m/z points that falls within a bin is described
by a value such as its average or maximum
intensity. Subsequently, the characteristics of
these bins (i.e., bin m/z location and esti-
mated intensity) are used as features for
data mining. The bins can be independent or
Fig. 2. Typical flowchart of the critical steps in data
mining and examples of techniques available for each
step. The ultimate goal is to draw conclusions based
on the data set.As shown, in general, there are several
options for each step. Sometimes the researchers may
consider using more than one particular approach in
each step.
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18 _______________________________________________________________________________ Thomas et al.
overlapping, equal-size, or adaptive. By
changing the bin size and binning method,
the researcher can empirically optimize the
feature extraction process.
Selecting and using only the significant fea-
tures for the modeling process makes the
entire data mining process more accurate and
effective. The data mining algorithm would be
faster for a data set comprising of fewer and
more relevant peaks and would result in
simple and meaningful results. Thus, it is
essential to remove the irrelevant and redun-
dant features to build better models. However,
it should be noted that feature selection pro-
cess does not always guarantee successful
selection of peaks for the classification prob-
lem. Therefore, it is necessary to validate the
features that are selected by increasing the
data sample size.
Feature Selection Techniques
Advances in machine learning have led to
the development of automated feature selec-
tion tools. There are two types of feature
selection techniques in practice today. One
type analyzes each feature independently
and eliminates features one at a time based
on how that feature correlates to the target.
Independent feature selection is a simple,
straightforward, and fast process. However,
it is often the case that a group of features
together correlate more strongly with the
desired target output. Consequently, the
assumption of feature independence can be
rather limiting. To address the previously
mentioned limitation, feature selection tech-
niques have been proposed where the features
are analyzed in groups/subsets. The correlation
between the groups of features is considered
with relation to the target output. Although the
process is computationally intensive, it capi-
talizes on the important feature interrelation-
ships discovering critical information that is
typically lost with the independent feature
Examples of independent feature selection
techniques (also called filter methods) are
receiver operating characteristics (ROC) anal-
ysis, statistical tests (i.e., t-test, Wilcoxon test,
test), wavelet transforms, information gain,
and so on. The grouped feature selection tech-
niques include stepwise feature selection,
genetic algorithms (GAs), correlation-based
feature selection, and principal component
analysis (PCA), to name a few. Feature selec-
tion is either pursued as a separate step before
decision modeling or as a step conducted in
parallel with decision modeling (called wrap-
per method) (9). Wrappers use the classifica-
tion algorithm as an integral part of the
feature selection process. In other words, the
feature selection process is optimized with
respect to the particular classifier at hand. The
following sections provide a brief description
of some feature selection techniques that have
been proposed for SELDI-MS proteomic anal-
ysis. Independent and grouped feature analysis
techniques are presented separately.
Independent Feature Selection
Statistical techniques based on t-, F, and χ
test statistics have been used in feature selec-
tion of SELDI-MS data. Liu et al. (21) used cri-
teria based on the previously listed statistics
for identifying discriminatory peaks from the
ovarian cancer data set (22). Normalization of
data during preprocessing was required prior
to performing t-test, whereas the other two
techniques were independent of this step.
Contrary, χ
test statistics were used for select-
ing the features to be used in the neural net-
work model for the detection of renal cancer
(23). Wilcoxon test was used by Sorace et al.
(24) for the ovarian cancer data set to rank fea-
tures and to understand the underlying prop-
erties of the data. Kozak et al. (25) performed
t-test and Wilcoxon test on the data set com-
prising ovarian and benign ovarian tumors.
ANOVA was used by Wagner et al. (26) on a
prostate cancer data set. The peaks prevalent
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in less than 30 samples were removed before
applying feature selection to the remaining
peaks. Mann-Whitney test has also been used
to rank features (27).
Similarly, ROC analysis has been exploited
by Adam et al. (28) and Qu et al. (29) as the
criteria to rank the peaks for healthy and
benign cases in prostate cancer detection
before classification using a DT. In both stud-
ies, a predefined threshold of the area under
the ROC curve (AUC) was selected as the
cutoff to determine significant peaks for fur-
ther classification. Briefly, AUC is a perfor-
mance index that summarizes the ROC curve.
The area under the ROC curve is always
bounded between 0.5 and 1. An area of 0.5 for
a peak suggests that it had no discriminatory
power and 1 suggests its ability to perfectly
differentiate between two groups. In the above
studies (28,29), there were no peaks with per-
fect AUC, which implied that no peak was
independently capable of discriminating per-
fectly the groups. ROC analysis will be dis-
cussed further under performance evaluation.
Mutual information is a feature selection
technique that is based on the information
content present in the features (30). Thus far, it
has been used in analyzing proteomic mass
spectral data for the prediction of lung cancer
(31). If a particular feature is strongly corre-
lated with the target output, it should have
high mutual information. The features with
low information content are eliminated in this
method. Depending on the underlying statis-
tical distribution of the data, mutual informa-
tion captures general statistical dependencies
between each feature and the target output.
Therefore, mutual information is better suited
to nonlinear decision analysis.
Wavelet transforms, popularly used in med-
ical imaging and general image processing,
have been successfully applied by Zhu et al.
(32) to proteomic data before being classified
with a recursive tree-based algorithm. Wavelet
analysis attempts to decompose the proteomic
spectra using multiscale filter banks. After
decomposition, large wavelet coefficients were
condensed to more significant and informative
coefficients that can distinguish between the
two groups based on their within-group and
between-group sum of squares (32). Wavelet
transform has also been used by Qu et al. (33)
for feature selection of SELDI-MS data prior to
constructing a linear discriminant analysis
(LDA) function. The objective of Qu’s study
was to determine if SELDI-MS could be used
for early prostate cancer detection.
Grouped Feature Selection
Stepwise analysis is a very popular linear
feature selection algorithm based on concepts
in linear statistics. As its name suggests, fea-
tures are reviewed and selected in a step-by-
step manner and at each step the variables are
evaluated to determine which contributes most
in differentiating between the groups. Stepwise
analysis compares the means of the different
peaks and selects the peaks with the maximum
difference in the mean values in the groups.
The next peak is added such that the power of
the model increases in combination with the
first one and this process continues and the
result of the “forward stepwise analysis” is a
list of peaks in their order of significance. This
process can also be done in reverse (backward
stepwise elimination). Initially, all variables are
included in the model. The variable that least
contributes to the classification is eliminated.
Thus, only highly contributing variables are
retained in the model. Li et al. (34) used for-
ward and backward stepwise feature selection
available in the ProPeak software to rank the
peaks for the breast cancer data. Stepwise
analysis and Wilcoxon tests were repeatedly
applied on the ovarian cancer data in ref. 24 to
select the features and the classification rules
weredeveloped by visual inspection and bin-
ning based on the p-values.
The simplicity and ease in understanding
the results of stepwise analysis has made it a
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20 _______________________________________________________________________________ Thomas et al.
very popular tool in feature selection. How-
ever, the stepwise method takes into consider-
ation only the linear relationships between the
peaks and does not account for any nonlinear
dependencies between them. Thus, stepwise
analysis cannot be considered an optimal
method if the features selected will be used to
develop a nonlinear model in ANN and simi-
lar nonlinear decision modeling algorithms. If
nonlinear components are present in the data
and they are important in the classification
problem, then linear feature techniques would
be less suited.
In such situations, computationally inten-
sive algorithms like those employed by GAs
would be better feature selection tools. GAs,
developed by researchers in the field of artifi-
cial intelligence (35) are based on certain
aspects of natural evolution, genetic inheri-
tance, and survival of the fittest. GAs explore
all possible subsets to obtain a set of features
that will discriminate between the groups of
training data. By operating on feature sets in
parallel, GAs obtain a global optimal result
and are known to be superior in generating
robust results for feature selection when pre-
sented with high-dimensional data. However,
the use of GAs is a computationally complex
process, especially when the number of fea-
tures is very large.
It should be noted that a GA does not
always guarantee success. Being a stochastic
system, there are situations when a fast con-
vergence occurs, which may halt the process
of evolution. There are practical limitations on
the number of iterations (generations) of a GA
and unlimited number of population size,
which may cause the GA to converge to a
local optimal solution. These algorithms are
nevertheless robust search algorithms that are
very powerful in discovering solutions for
complex high-dimensional problems where
the search space is complex and poorly under-
stood and where traditional methods fail. In
general, GAs maintain and evaluate potential
solutions, generate better and robust solutions,
and thereby improve the quality of decision
making. Conrad et al. (36) and Petricoin et al.
(37) used GAs for peak selection and self-
organizing maps for classification of SELDI-
MS proteomic data in cancer diagnosis.
PCA is an unsupervised feature selection
technique used to summarize data in high-
dimensional space into a few dimensions. Each
dimension or principal component represents
a linear combination of the original variables.
The first principal component accounts for
most of the variability of the data. The next
principal component accounts for the variabil-
ity not accounted for by the first component
and so on. Typically, the first few principal
components are identified and then the data
set is projected onto these components for
dimension reduction. There are several appli-
cations of PCA for feature selection in MS data
(38–41). For example, Lilien et al. used a spe-
cific algorithm called Q5 in which PCA was
used for feature selection and LDA for model
development (38).
Other than an exhaustive search of every
possible feature combination, there is no par-
ticular feature selection strategy that guaran-
tees optimal results. Consequently, it is
advised to explore a variety of strategies given
a particular proteomic data set. For example,
Liu et al. (21) investigated several feature
selection and classification techniques using a
publicly available ovarian cancer data set.
Specifically, three feature selection techniques
method, t-test, and correlation analysis) were
used in their study. The features selected by
these techniques were then fed into diverse
classification methods such as support vector
machines (SVM), DTs, k-nearest neighbor
(KNN), and naïve-Bayesian algorithms. Results
with and without the feature selection phase
were acquired to determine the importance
of the feature selection process. The study
clearly demonstrated that feature selection
is a critical step but it should always be
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considered in relation to the classification
step. For this particular study, SVM classifi-
cation algorithm and KNN reported the best
classification accuracy (100%) when used
with the peaks selected by correlation-based
feature analysis. In contrast, models built by
randomly selecting features from the data
resulted in poor accuracy.
In another study, Li et al. (42) compared the
performance of a SVM classifier using the filter
approach (a statistical test using a distance
measure) and GAs in ovarian cancer detection
and diagnosis. The main goal of the study was
to demonstrate the feasibility of applying arti-
ficial intelligence techniques on serum pro-
teomic data. In this study, GAs emerged as the
superior feature selection strategy when used
in combination with a SVM classifier.
To summarize, there is no general guide-
line on which feature selection strategy
should be used. It is generally understood
that grouped feature selection techniques
that are capable of capturing nonlinear rela-
tionships among the available features
should be used in combination with non-
linear models. Typically, researchers should
investigate various techniques to empirically
optimize the feature selection process. Once
the features are selected, data modeling is the
next step.
Classifiers and Data Modeling
Humans and animals acquire the ability to
learn and recognize through interaction with
the environment. Learning from data has
been an area of interest for researchers in
statistics and computer science. Machine
learning algorithms are able to make infer-
ences about a sample of data through famil-
iarization and repeated interaction with the
data. These algorithms vary in their training
techniques, final goal, and representation of
A learning process would typically com-
prise of the task of learning and developing
rules or functions from the given data set of
samples. The development of mathematically
accurate rules and functions to describe data
is called data modeling. The model developed
identifies the properties of the different classes
and what separates them to make a correct
classification. In the next phase, called testing,
the developed model is validated with new
data to verify that the model produces accu-
rate results. The learning phase and estimation
of the model is implemented and described
using different learning methods or algo-
Afunction that describes or approximates
the data is of the form f (X, w) where X is the
input and w is a parameter of the function.
The function f can be linear or nonlinear.
Machine learning classifiers that are based on
nonlinear decision function include ANN, self-
organizing maps (SOMs), and GAs. LDA is an
example of a linear classifier.
Machine learning algorithms are of two
types—supervised and unsupervised. In super-
vised learning (also called “learning with a
teacher”), prior knowledge is available about
the class to which each case (sample) belongs.
The training data set comprises of input
values and their corresponding output classes
(provided by teacher). During the training
phase, the training data is used to determine
how the features are to be selected, weighed,
and combined so as to discriminate between
the classes. The testing phase involves appli-
cation of the weighted features to classify a
new test data whose class is not known and
which the decision model has not seen before.
Thus, the goal of classification methods is to
build models of the data set in hand and use
that model to classify new samples. The pro-
cess of learning would involve creating a
model so that the predictions of the model are
close to the desired target. If the model is able
to classify new data correctly we have reason
to believe that it is a good model. A wide
range of algorithms has been developed for
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22 _______________________________________________________________________________ Thomas et al.
supervised learning (DTs, SVM, logistic regres-
sion, et al.).
In unsupervised learning (“learning with-
out a teacher”), the group to which each
sample belongs is unknown or ignored and
data is grouped together based on similarity
measures. The learning process involves no
teacher and the algorithm must identify pat-
terns in the data. Often, unsupervised learning
may lead to more than one possible solution.
Clustering and Kohonen’s SOMs are typical
examples of unsupervised learning that have
been used in studies analyzing mass spec-
troscopy data. ANNs, on the other hand,
come as supervised and unsupervised learn-
ing algorithms.
In both learning techniques, the goal is to
predict (classify) or describe the data by devel-
oping models of the data, which are then used
to classify or describe new cases. If the data
has just two or three features, then classifying
data would be easy. However, developing
models can be a daunting task if there are
many features to analyze. High-dimensional
data is not only hard to visualize, but all pos-
sible combinations should be considered by
exhaustive searching techniques during the
training phase when the model is developed.
A large number of dimensions with very few
samples leads to what is often referred to as
over-fit or over-trained models. Over-fit models
cannot generalize and fail to classify new
cases with the desired accuracy.
Supervised Classifiers
LDA is a supervised linear modeling
approach and was one of the first to be
applied to proteomic mass spectral data. LDA
is a noniterative and deterministic classifica-
tion approach. LDA analysis computes exact
solutions, it is simple to implement, and easy
to understand. LDA is typically inefficient in
classifying high-dimensional data and thus
feature selection techniques should be applied
before any modeling can be done. LDA
captures potential linear relationships in the
data and represents it in the form of a linear
function. However, nonlinear and complex
relationships cannot be fully captured when a
linear classifier is used.
LDA has been used in SELDI-MS pro-
teomic studies conducted by Lilien et al.
where the LDA was a part of their Q5 algo-
rithm (38). Wagner et al. (26) analyzed prostate
cancer data using both LDA and QDA
(quadratic discriminant analysis). The feature
reduction step helped in reducing the number
of peaks by 97%. In another study, Qu et al.
(34) analyzed prostate cancer data comprising
45,538 peaks. They developed an LDA model
using 12 peaks selected by wavelet transfor-
mation and a procedure based on Mahanobis
DT analysis is another popular supervised
classification technique (9). The resulting
model is in the form of a hierarchical tree
structure. The main advantage of DTs is that
they are expressed as a set of rules. As they
are visually presented in an easy to interpret
form, DTs are very popular in several domains
including proteomics. A DT’s goal is to iden-
tify a set of variables (peaks) that can be used
to classify cases or samples into specific
groups. A DT performs classification through
a systematic process referred to as recursive
partitioning (9). At each node of the tree, a
test (presence/absence/intensity of peaks) is
applied to one or more variables (peaks) that
will have one of two outcomes. The outcome
will lead to a split into a leaf node or to
another decision node where another test is
applied. Peaks are selected for a split based on
a cost function, which is the measure of het-
erogeneity of the descendant nodes. The cost
function is often an entropy measure or gain
in information. The splitting is made and the
tree model is built until there is no gain in
information associated with a split or if the
there are no more nodes left. Finally, classifi-
cation of new test data is done by following
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the tree rules (i.e., by moving through the tree
until a leaf is encountered).
Popular programs for constructing DTs are
C4.5 (43) and classification and regression tree
(CART) (44). The C4.5 algorithm was used by
Won et al. (37) to classify the data set of 36
samples and 119 peaks into renal cell carci-
noma (RCC) and healthy patients. The 119
peaks were identified by preprocessing using
the Ciphergen Protein Chip software. The DT
identified five discriminatory peaks for model
development. A CART model was developed
by Markey et al. (46) to classify 41 serum
samples with 21 peaks into lung cancer and
controls. The peaks used in this model were
identified by mass spectrometry. Adam et al.
(28) used CART to classify serum proteomic
patterns identified by Ciphergen System soft-
ware (i.e., the Biomarker Wizard), into prostate
cancer, benign prostate hyperplasia (BPH),
and controls. Three hundred and twenty-six
serum samples from 167 PCA patients, 77 BPH
patients, and 82 healthy men were used in this
particular study. More than 60,000 peaks in the
range of 2000–40,000 Da were selected by the
SELDI software and was reduced to 772 by
preprocessing and then to 124 by peak selec-
tion using ROC analysis. Finally, the DT
selected nine peaks to develop the model.
Biomarker pattern software (BPS) based on the
CART decision model was used by Zhang
et al. (47) to classify 156 urine samples into
transitional cell carcinoma, benign urogenital
diseases, and controls. The process started with
peak detection phase using the Biomarker
Wizard software. A signal-to-noise ratio of 5
was used to filter the peaks that were in the
range 2000 to 50,000 Da. Four hundred peaks
produced by peak detection were used in DT
construction. The training algorithm identi-
fied five discriminatory peaks and developed
the tree model. BPS was also used by Kang
(48) to identify serum biomarkers that distin-
guish between severe acute respiratory syn-
drome (SARS) and non-SARS samples.
Overall, DTs are easy to interpret and can be
represented in the form of if–then rules. The
training and testing times are reasonable when
compared with neural networks and they can
handle large number of features. Despite these
advantages, they are not recommended when
there are large amounts of missing data and in
problems involving complex data distributions.
The instability associated with DTs causes small
changes in data to result in entirely different
decision rules. Therefore, interpreting DT results
should be exercised with caution.
Thus far, the discussion was on supervised
linear classifiers. Another popular classifier
that has been used in SELDI-MS protein data
analysis is the ANN (49). ANNs are powerful
nonlinear classifiers, based on artificial intelli-
gence principles. Inspired by the human brain,
they comprise of highly connected and non-
linear units referred to as neurons that are
connected by weights. The interunit connec-
tion weights stores the processing ability of
the neural network. This is obtained by the
process of learning from the training data
set. There are two different types of neural
networks—supervised neural networks and
unsupervised neural networks.
By far, the most popular ANN architecture
in medical decision making has been the tradi-
tional backpropagation neural network, intro-
duced by Rumelhart et al. (50). A simple
representation of a neural network is as a set
of interconnected layer of neurons with
weighted interconnections reflecting various
influences of each neuron on the others (Fig. 3
illustrates a simplified neural network model).
Training is performed by using a data set with
known inputs and outputs. In the backpropa-
gation training algorithm, the neural network
output is compared with its expected output
for each training example. Computation starts
at the output layer and propagates backward
to the hidden layer(s). The difference between
the desired output and actual output (error) is
calculated and propagated back through the
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24 _______________________________________________________________________________ Thomas et al.
network. The neural network weights are
adjusted in an iterative manner until the net-
work achieves a predefined threshold of min-
imum squared error for the whole training set.
As with all supervised classifiers, develop-
ment of an ANN involves both the training
and validation phases of the network. During
the learning phase, the data is presented
repeatedly to the ANN and it learns from that
by storing the decision rules as weights. Fol-
lowing the training phase is the validation
phase where new cases are presented for clas-
sification. Rogers et al. (23) used 48 data sam-
ples from patients with RCC, 38 controls, and
20 benign cases to train a feed-forward neural
network model. They reported sensitivity and
specificity values in the range 98.3 to 100%
during training and 81.8 to 83.3% during test-
ing. However, further testing was performed
later to assess the robustness of the model
with a group of 80 samples to get sensitivity
and specificity in the 41 to 76.6% range, which
called for further evaluation of contributing
factors. SELDI-MS coupled with ANN was
used by Mian et al. (51) to identify protein pat-
terns associated with either control/drug treat-
ment for chemotherapy-sensitive cells or
response/nonresponse for chemotherapy-
resistant breast cancer cells, respectively for two
popular chemotherapeutic drugs. The model
developed was able to classify the test data as
resistant or as sensitive to a particular drug
and discriminate between chemotherapy-
sensitive and -resistant breast cancer cells with
high accuracy. Ball et al. (52) used multilay-
ered perception ANN with a backpropagation
algorithm to mine the SELDI-analyzed data of
tumors and controls. Poon et al. (53) used
neural networks to discriminate hepatocellular
carcinoma (HCC) from chronic liver disease
(CLD). Two hundred and fifty significant
peaks identified by significance analysis of
microarrays software were used to develop a
feed-forward backpropagation ANN model.
ROC curve analysis showed that the ANN
was useful in differentiating HCC and CLD
cases regardless of serum concentrations. They
reported a sensitivity and specificity for HCC
and CLD cases as approx 95% and approx
90%, respectively.
However, ANNs are not devoid of limita-
tions. The main criticism regarding ANNs is
their “black box” nature. It is very difficult to
determine how the neural network makes the
decisions in classifying data. An additional
disadvantage is their low computational effi-
ciency during the training phase. The optimal
theoretical size of data required for training
and ANN is almost never met in medical
research. Small sample size often leads to over-
fitting. Furthermore, the architecture of the
ANN for getting the best results is subjective.
Changes in the ANN architecture, training
parameters, and starting point of training will
result in different models and solutions. The
training algorithms often get trapped in the
local minima resulting in suboptimal results.
Despite all the issues surrounding the archi-
tecture and training of ANNs, this group of
classifiers is known to produce generalized
results and often report better accuracy than
traditional statistical techniques in medical
diagnosis. In addition, neural networks can
handle problems with large amounts of
diverse features and are known to perform
well with complex data distributions.
Fig. 3. A schematic representation of an artificial
neural network.
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Unsupervised Classifiers
Clustering is an unsupervised learning
technique (9), wherein data samples are
grouped into clusters, based on a measure of
association so that similar groups are in one
cluster. Input to cluster analysis is a set of
samples and a measure of association (simi-
larity or dissimilarity between two samples).
The output is a group of clusters and a gen-
eralized description of each cluster. Hierar-
chical clustering is a popular clustering
algorithm (9) that has been used in several
SELDI studies. The data is grouped into a
sequence of clusters in a bottom-up manner
and the result is displayed in the form of a
dendrogram or a tree structure. The number
of clusters in the data is not prespecified in
the problem. Agglomerative hierarchical clus-
tering is a type of hierarchical clustering
algorithm where each sample is regarded as a
cluster. In the agglomerative clustering tech-
nique, pairs of clusters closer to each other are
merged based on their similarity measure and
this is repeated until the entire data is in one
Poon et al. (53) used two-way hierarchical
clustering to differentiate HCC from CLD.
The result of the study reported was that the
algorithm separated HCC and CLD cases into
two major clusters. However, there was no
mention of the model evaluation. Purohit et
al. (38) performed hierarchical cluster analy-
sis for classifying data into diseased and
healthy. The PCA feature selection method
was first applied on the data. This combina-
tion of PCA and cluster analysis could cor-
rectly identify 68% of the patients and 100%
of the healthy group. They indicated the pos-
sibility of the diseased people to be in a dif-
ferent disease stage.
SOM is a nonlinear clustering technique and
was first introduced by Kohonen (54). Similar
in design to the human brain, an SOM consists
of interconnected neurons and has two layers;
the input layer, which contains the raw data
and the output layer, which comprises of the
clusters. The process of learning involves suc-
cessive passes through the input layer until
no new information is obtained on subse-
quent passes. The result of this process is a set
of clusters that best represent the differences
in the data. SOMs follow unsupervised learn-
ing and depend on the self-organizing nature
of the data for the formation of best set of
SOMs have been used in the Petricoin
et al. (22) study in combination with GAs to
identify ovarian cancer biomarkers and to
classify the samples into healthy, benign, and
cancer cases. The clustering algorithm was
trained on 50 healthy women and 50 women
in different stages of ovarian cancer and tested
on 116 new cases, yielding a sensitivity of
100% and specificity of 95%.
In samples with categorical data, KNN may
be used to compute the similarity between
samples and clusters. This technique is based
on the distances from immediate neighbors.
The class of an input pattern is chosen as the
class of the majority of its KNN. Euclidean
and Mahanobis distances are often used as the
distance metrics (9).
There are several clustering algorithms
available in the literature and could easily con-
found a new researcher trying to get a suitable
algorithm. All clustering algorithms will pro-
duce clusters in a given data irrespective of
whether or not they exist. Therefore, cluster
algorithms should only be applied on a data
set that is expected to contain clusters. Once
clusters are formed out of the data, they
should be evaluated using existing validation
techniques (external and internal criterion
analysis) for cluster validation. However,
existing validation techniques are subjective
in nature and data analysts should keep in
mind that there is no best clustering algo-
rithm. Therefore, empirical study is advised
by exploring several algorithms on a given
data set.
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26 _______________________________________________________________________________ Thomas et al.
Combining Classifiers
Although most of the recent proteomic
studies have been performed using individual
classifiers, the approach of combining classifiers
is a promising strategy to reduce the classifica-
tion prediction error and to improve decision-
making performance. The final classifier is a
set of base classifiers, each of which makes its
own classification and they are combined to
constitute the single classification result of the
entire classifier.
Breiman (55) refers to classifiers as unsta-
ble if a small change in data causes large
changes in classification and they often have
high bias and low variance. The opposite is
true for stable classifiers. DTs and neural nets
are considered to be unstable classifiers,
whereas LDA and KNN are considered to be
stable. If classifiers are combined, the variance
and error are usually lowered. Bagging and
boosting are two such techniques that have
been used for combining classifiers in data
mining and they have also been applied in
proteomic data analysis.
Boosting of weak classifiers is performed by
taking a weighted vote of each classifier.
Boosting applies the training algorithm
sequentially to the training sample and the
sample is reweighed to give importance to
cases that are not correctly predicted. A
weighted majority vote of the classifiers is
taken for the decision making. This method
often results in a stronger classifier and is very
effective in DTs, which are considered to be
unstable classifiers, as it boosts the perfor-
mance of existing weak learners, reduces the
test error and variance, avoids over-fitting,
and thus leads to an improvement of perfor-
mance. For example, a boosted DT was used
in the early detection of prostate cancer in the
studies conducted by Qu et al. (29). The study
aimed at developing a classifier to discrimi-
nate men with prostate cancer from those with
BPH and controls.
Bagging is another technique that has been
used to combine predictors and was first used
by Breiman in data mining applications. Many
replicates of the available data set having the
same size as the original data set are drawn
with replacement from the training sample
(called bootstrap samples). The learning algo-
rithm is then applied to each bootstrap sample
and validation is then performed on the sam-
ples where training is not done. The final clas-
sification is completed by taking a majority
vote where each classifier has equal weight in
voting. When the individual results from each
classifier vary considerably from one another,
taking an average of the results will result in a
more accurate prediction. However, if the
results are similar, averaging the results may
not result in a better performance. Thus, the
advantage of bagging is clearly when the per-
formance of individual classifiers is uncorre-
lated. Izmirlan (56) used bagging in a proteomic
random forest classification algorithm where
DTs were bagged using 632 cross-validation
and randomly selected feature sets.
The result of combining classifiers often
improves performance, especially when com-
bining classifiers that do not think alike and
have low correlation. Using a combined
approach, individual classifiers complement
each other by capturing information that indi-
vidually they miss.
One of the major challenges faced in the
application of machine learning algorithms to
medical data is the validation of a trained
model with new test data. The process of deci-
sion modeling requires that the model be
developed by training on a certain set of the
data (training set), which is followed by the
validation of the model on another set of data
not previously seen during training (testing
set). The obvious way to handle this issue is by
dividing the data into train and test sets before
model building by stratified random sampling.
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Data Mining In Proteomic MS ________________________________________________________________ 27
Stratified random sampling was used in refs. 28
and 45. These studies report the performance
of the decision models based on a single train-
test data split.
However, medical data is quite often very
difficult and expensive to acquire. Thus, there
are not enough available cases to divide into
train-test subsets. Furthermore, the noise
inherent in most medical data and the com-
plex relationships between features requires a
sample size large enough to efficiently model
the data with accuracy. In addition, the size of
the test set controls the statistical power and
confidence in the developed decision model.
Consequently, sophisticated sampling strate-
gies are required to capitalize on the available
data (57). The following is a brief description
of data sampling strategies recommended in
medical decision making.
Cross-validation is by far the most popular
data sampling strategy. The data is randomly
divided into two sets. The decision model is
trained on the first and tested on the second.
This random splitting process is repeated sev-
eral times to reduce the selection bias. The
average of all the test estimates gives the
average error of the model. If the data set
used for training is too small, the model may
not be able to predict test cases well. A small
test set may not result in an accurately vali-
dated classifier and can have a large error
rate. Consequently, different train–test ratios
(e.g., 50–50%, 75–25%, etc.) are explored with
A common implementation is the k-fold
cross-validation. The data is partitioned into
k-disjoint sets. Training of the classifier is done
on the k-1 sets and testing on remaining one
data set. This is done for all the k-subsets
resulting in k-models and the estimated error
will be the average of the k-error rates. For
example, 10-fold cross-validation divides the
data into 10 groups. Nine groups are used for
training and testing is done on the left out-
group. This is repeated 10 times until each of
the 10 groups has served as the test group.
The average test error of the 10 groups is the
final test error estimate and gives an approxi-
mate idea of the quality of the model for the
classification of the data. K-fold cross-validation
requires the careful random stratification of the
10 groups. Ten-fold cross-validation was used
in ref. 57 to discriminate between early-
stage melanoma patients with and without
melanoma recurrence. The sensitivity and
specificity were 72 and 75%, respectively.
Wagner et al. (26) stressed the significance of
cross-validation with various decision models.
Their study showed results based on a 90- to
10%-data split, randomly repeated 100 times.
Zhang et al. (47) employed the same data sam-
pling strategy.
A noteworthy special case is the popular
leave-one-out cross-validation approach, a spe-
cial case of the k-fold cross-validation imple-
mentation. If the data comprises of k-samples,
the data model is trained using the k-1 samples
and tested on the remaining one sample. This is
repeated k times until every sample has served
as a test case. The average error on the k sam-
ples is the estimated test error. The main advan-
tage of the technique is that the decision model
uses almost all available cases for training with-
out compromising the statistical significance of
the testing phase. However, the technique can
be time consuming with large sample sizes and
elaborate decision models. Therefore, it is used
predominantly in resampling of small data sets.
There are several published examples of
studies using variations of the cross-validation
sampling scheme (see refs. 32,38,39,42,46,59).
In a comparative study, Zhu et al. (32) used
both leave-one-out and k-fold cross-validation
to assess the validity of their DT model and
achieved an error rate of 14.5 and 20% for
leave-one-out and k-fold cross-validation,
respectively showing that leave-one-out has a
lower bias than the latter.
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28 _______________________________________________________________________________ Thomas et al.
The bootstrap method involves the genera-
tion of a training set by sampling with replace-
ment. This is done n times for a data set of n
cases. The data is trained on the bootstrap set
and tested on the original set. It is recom-
mended that the process is repeated many
times (>200). The final performance estimate
is the average of all bootstrap estimates. This
is a computationally intensive process even for
small size data sets. Mian et al. (51) used boot-
strap validation with 86 independent ANN
models. The variation in the model’s perfor-
mance, accuracy of classifying test data, and
identification of potential outliers were
assessed through the bootstrap resampling.
Performance Assessment of Models
The last phase of the data mining process is
the assessment of the models developed by
the previously described machine learning
algorithms. The different methods for evaluat-
ing the models’ ability to classify new test
cases are discussed below.
Accuracy of classification is calculated by
taking the ratio of the number of correctly clas-
sified samples to the total number of samples
in the test data. However, when the prevalence
of a particular class is higher than the other
class, the majority class will cause a bias in the
result. In such a scenario, the accuracy measure
can be misleading. SELDI-MS-based proteomic
studies that have used accuracy to report results
include those in refs. 25,31,39,42,51, and 52.
In two-class samples, there are four possi-
ble outcomes when the decision model is
tested. They are true-positive, true-negative,
false-positive, and false-negative results. Sen-
sitivity (true-positive rate) is the ratio of the
number of correctly classified positive samples
over the total number of positive samples.
High sensitivity is much desired in medical
diagnosis where the impact of wrongly pre-
dicting a diseased person as healthy is high.
False-positive rate is the probability that a
healthy subject is wrongly classified as dis-
eased (referred to as specificity). High speci-
ficity is desirable where a false alarm would
result in unwanted elaborate tests and treat-
ments. Ideally, for perfect classification, both
sensitivity and specificity should be 1 (100%).
The clinically acceptable sensitivity and speci-
ficity depends on the application.
Several studies have reported their results
using sensitivity and specificity as the perfor-
mance indices (see refs. 23,24,28,33,45–47). The
main limitation of using sensitivity and speci-
ficity as the only evaluation indices is their
dependence on class prevalence and the deci-
sion threshold. Therefore, it is difficult to
directly compare the results of studies that are
reported using only sensitivity and specificity
ROC Analysis
Based on the classical signal detection
theory, ROC analysis reports the sensitivity
and specificity of a decision model for all pos-
sible decision thresholds. Conventionally, a
ROC curve plots the true-positive fraction (or
sensitivity) vs the false-positive fraction (or [1-
specificity]) for a wide and continuous range
of decision thresholds and provides a more
meaningful and valid measure of classification
performance. Furthermore, ROC curves can be
used to determine optimum decision levels
that maximize accuracy, average benefit, or
other measures of clinical efficacy. The AUC
gives a complete picture of the performance of
a model and can be used to compare the per-
formance of multiple models. The more the
curve is shifted to the upper left corner of the
graph (specificity = sensitivity = 1), the better the
diagnostic performance. The area index varies
between 0.5 (representing chance behavior) and
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Data Mining In Proteomic MS ________________________________________________________________ 29
1.0 (representing perfect performance). Gener-
ally, a higher value of the area index indicates
better performance. More importantly, ROC
analysis is threshold and prevalence indepen-
dent. Figure 4 shows typical ROC curves.
The usage of ROC in data mining is still
below its full potential. It is highly recom-
mended that researchers embrace this tech-
nique, since comparison of results across
different studies is straightforward. ROC anal-
ysis has been utilized in several SELDI pro-
teomic studies (25,28,34,42,46).
Data mining is a data-driven process where
the results obtained largely depend on the
data being analyzed. The methods employed
for feature selection, classification, data sam-
pling, and performance evaluation drive the
process and alter final results. Thus, it is
recommended to explore more than one tech-
nique to make comparisons and better under-
stand the problem in hand. The promise and
opportunities of combination of data mining
approaches and the generated proteomic mass
spectrometry data for discovery of novel
biomarkers with diagnostic value is obvious.
However, caution should be exercised in
application applying various data mining
techniques in this regard. This review made an
effort to reinstate the critical issues and limita-
tion of data mining applications to be
addressed by researchers analyzing SELDI-MS
proteomic data for extracting clinically useful
Funding support for AT was in part by a
grant from NIH-NCRR 5 P20 16480 (principal
investigator: Nigel Cooper).
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