J. Biomedical Science and Engineering, 2010, 3, 247-252
doi:10.4236/jbise.2010.33033 Published Online March 2010 (http://www.SciRP.org/journal/jbise/
Published Online March 2010 in SciRes. http://www.scirp.org/journal/jbise
A comparison study between one-class and two-class machine
learning for MicroRNA target detection
Malik Yousef1,2,3*, Naim Najami1,4, Waleed Khalifa1*
1The Institute of Applied Research-the Galilee Society, Shefa Amr, Israel;
2Computer Science, The College of Sakhnin, Sakhnin, Israel;
3Al-Qasemi Academic College, Baqa Algharbiya, Israel;
4The Academic Arab College of Education, Haifa, Israel.
Email: yousef@gal-soc.org; khwalid@hotmail.com; najamina@gmail.com
Received 9 September 2009; revised 2 November 2009; accepted 5 November 2009.
The application of one-class machine learning is
gaining attention in the computational biology com-
munity. Different studies have described the use of
two-class machine learning to predict microRNAs
(miRNAs) gene target. Most of these methods require
the generation of an artificial negative class. However,
designation of the negative class can be problematic
and if it is not properly done can affect the perform-
ance of the classifier dramatically and/or yield a bi-
ased estimate of performance. We present a study
using one-class machine learning for miRNA target
discovery and compare one-class to two-class ap-
proaches. Of all the one-class methods tested, we
found that most of them gave similar accuracy that
range from 0.81 to 0.89 while the two-class naive
Bayes gave 0.99 accuracy. One and two class methods
can both give useful classification accuracies. The
advantage of one class methods is that they don’t re-
quire any additional effort for choosing the best way
of generating the negative class. In these cases one-
class methods can be superior to two-class methods
when the features which are chosen as representative
of that positive class are well defined.
Keywords: MicroRNA; One-Class; Two-Class; Machine
MicroRNAs (miRNAs) are single-stranded, non-coding
RNAs averaging 21 nucleotides in length. The mature
miRNA is cleaved from a 70-110 nucleotide (nt) “hair-
pin” precursor with a double-stranded region containing
one or more single-stranded loops. MiRNAs target mes-
senger RNAs (mRNAs) for cleavage, primarily by re-
pressing translation and causing mRNA degradation [1].
Although recent findings [2] suggest microRNAs may
affect gene expression by binding to either 5’ or 3’ un-
translated regions (UTRs) of mRNA, most studies have
found that miRNA mark their target mRNAs for degra-
dation or suppress their translation by binding to the
3’UTR and most target programs search there. These
studies have suggested that the miRNA seed segment
which includes 6-8 nt at the 5’ end of the mature miRNA
sequence is very important in the selection of the target
site (see Figure 1).
Several computational approaches have been applied
to miRNA gene prediction using methods based on se-
quence conservation and/or structural similarity [3,4,5,
6,7]. Those methods that used machine learning were
based on the two-class approaches, while our new reported
results are based on the one-class approaches.
Several additional methods for the prediction of mi-
RNA targets have been subsequently developed. These
methods mainly use sequence complementarities, ther-
modynamic stability calculations, and evolutionary con-
servation among species to determine the likelihood of a
productive miRNA: mRNA duplex formation [8,9]. John
et al., (2004) developed the miRanda [10] algorithm for
miRNA target prediction. MiRanda uses dynamic pro-
gramming to search for optimal sequence complemen-
tarities between a set of mature miRNAs and a given
miRNA. Another algorithm RNAhybrid [8,9] is similar
to a RNA secondary structure prediction algorithm like
the Mfold program [11] but it determines the most fa-
vorable hybridization site between two sequences.
Lewis et al., (2005) developed TargetScanS [12]. Tar-
getScanS scores target sites based on the conservation of
the target sequences between five genomes (human,
mouse, rat, dog and chicken) as evolutionarily conserved
target sequences are more likely to be true targets. In
testing, TargetScanS was able to recover targets for all
5300 human genes known at the time to be targeted by
PicTar [13] is a computational method to detect com-
on miRNA targets in vertebrates, nematodes (C. ele- m
248 M. Yousef et al. / J. Biomedical Science and Engineering 3 (2010) 247-252
Copyright © 2010 SciRes. JBiSE
3’ uagcgccaaauaugg
5’ has-miR-579
||: | |||||: ||||||:|
5’ atttctttttat
Figure 1. Duplex partitioned into two parts for miRNA hsa-miR-579 and its target
LRIG3, the seed part and the out-seed part. The seed part appears in capital letters.
gans), and insects (Drosophila melanogaster). PicTar is
based on a statistical method applied to eight vertebrate
genome-wide alignments (multiple alignments of ortho-
logous nucleotide sequences (3' UTRs). PicTar was able
to recover validated miRNA targets at an estimated 30%
false-positive rate. In a separate study PicTar was ap-
plied to target identification in D. melanogaster [14].
These studies suggest that one miRNA can target 54
genes on average and that known miRNAs are projected
to regulate a large fraction of all D. melanogaster genes
(15%). This is likely to be a conservative estimate due to
the incomplete input data.
TargetBoost [15] is a machine learning algorithm for
miRNA target prediction using only sequence information
to create weighted sequence motifs that capture the
binding characteristics between miRNAs and their tar-
gets. The authors suggest that TargetBoost is stable and
identifies more of the already verified true targets than
do other existing algorithms.
Sung-Kyu et al., (2005), also reported the development
of a machine learning algorithm using Support Vector
Machine (SVM). The best reported results [16] were
0.921 sensitivity and 0.833 specificity. More recently
Yan and others, used a machine learning approach that
employs features extracted from both the seed and
out-seed segments [17]. The best result obtained was an
accuracy of 82.95% but it was generated using only 48
positive human and 16 negative examples, a relatively
small training set to assess the algorithm.
In 2006, Thadani and Tammi [18] launched MicroTar,
a novel statistical computational tool for prediction of
miRNA targets from RNA duplexes which does not use
sequence homology for prediction. MicroTar mainly
relies on a quite novel approach to estimate the duplex
energy. However, the reported sensitivity (60%) is sig-
nificantly lower than that achieved using other published
algorithms. At the same time, a miRNA pattern discov-
ery method, RNA22 [19] was proposed to scan UTR
sequences for targets. RNA22 does not rely upon cross-
species conservation but was able to recover most of the
known target sites with validation of some of its new
More recently, Yousef et al., (2007) described a target
prediction method, (NBmiRTar [20]) using instead ma-
chine learning by a Naïve Bayes classifier. NBmiRTar
does not require sequence conservation but generates a
model from sequence and miRNA:mRNA duplex infor-
mation derived from validated target sequences and arti-
ficially generated negative examples. In this case, both
the seed and “out-seed” segments of the miRNA:mRNA
duplexes are used for target identification. NBmiRTar
technique produces fewer false positive predictions and
fewer target candidates to be tested than miRanda [10].
It exhibits higher sensitivity and specificity than algo-
rithms that rely only on conserved genomic regions to
decrease false positive predictions.
This paper describes a comparison study of using one-
class and two-class approaches for miRNA target detec-
tion. The advantage of one class methods is that they
don’t require any additional effort for choosing the best
way of generating the negative class while it is clear that
the two class approaches performances are outperform
the one-class methods.
2.1. Designing Duplex Structure and Sequence
Machine learning enables one to generate automatic
rules based on observation of the appropriate examples
by the learning machine. However, the selection and
design of the features that will be considered in order to
represent each example for the learning process are very
important and influence the classifier performance. We
have followed [20] for feature design. We have parti-
tioned the duplex into two parts, the seed (5’ 8nt of the
miRNA) and out-seed (3’ remainder) as described in
Figure 1. For each of these parts the following features
are extracted to give 57 structural features: 1) the num-
ber of paired bases (bp), 2) The number of bulges (in-
serts on one strand between paired bases), 3) the number
of loops (unpaired bases opposite each other between
paired bases), 4) the number of asymmetric loops (loops
with unequal numbers of unpaired bases on the two
strands), 5) eight features, each representing the number
of bulges of lengths 1-7 and those with lengths greater
than 7. 6) Eight features, each representing the number
of symmetric loops with lengths 1-7 and those with
lengths greater than 7, 7) eight features each represent-
ing the number of asymmetric loops with lengths 1-7
and those with lengths greater than 7, 8) the distance
from the start of the seed (the 3’ end) to the first paired
base of the 5’ start of the out-seed part is an additional
feature that is extracted. For the sequence features, we
define “words” as sequences having lengths equal to or
less than 3. The frequency of each word in the seed part
M. Yousef et al. / J. Biomedical Science and Engineering 3 (2010) 247-252
Copyright © 2010 SciRes.
is extracted to form a representation in the vector space.
2.2. One-Class Methods
In general a binary learning (two-class) approach to
miRNA discovery considers both positive (miRNA) and
negative (non-miRNA) classes by providing examples
for the two-classes to a learning algorithm in order to
build a classifier that will attempt to discriminate be-
tween them. The most common term for this kind of
learning is supervised learning where the labels of the
two-classes are known before hand. One-class uses only
the information for the target class (positive class)
building a classifier which is able to recognize the ex-
amples belonging to its target and rejecting others as
Among the many classification algorithms available,
we chose five one-class algorithms to compare for
miRNA discovery. We give a brief description of each
one-class classifier and we refer the references [21,22]
for additional details including a description of pa-
rameters and thresholds. The LIBSVM library [23]
was used as implementation of the SVM (one-class
using the RBF kernel function) and the DDtools [24]
for the other one-class methods. The WEKA software
[25] was used as implementation of the two-class
2.3. One-Class Support Vector Machines (OC-SVM)
Support Vector Machines (SVMs) are a learning ma-
chine developed as a two-class approach [26,27]. The
use of one-class SVM was originally suggested by [22].
One-class SVM is an algorithmic method that pro-
duces a prediction function trained to “capture” most
of the training data. For that purpose a kernel function
is used to map the data into a feature space where the
SVM is employed to find the hyper-plane with maxi-
mum margin from the origin of the feature space. In
this use, the margin to be maximized between the two
classes (in two-class SVM) becomes the distance be-
tween the origin and the support vectors which define
the boundaries of the surrounding circle, (or hy-
per-sphere in high-dimensional space) which encloses
the single class.
2.4. One-Class Gaussian (OC-Gaussian)
The Gaussian model is considered as a density estima-
tion model. The assumption is that the target samples
form a multivariate normal distribution, therefore for a
given test sample z in n-dimensional space, the probabil-
ity density function can be calculated as:
are the mean and covariance matrix
of the target class estimated from the training samples.
2.5. One-Class Kmeans (OC-Kmeans)
Kmeans is a simple and well-known unsupervised ma-
chine learning algorithm used in order to partition the
data into k clusters. Using the OC-Kmeans we describe
the data as k clusters, or more specifically as k centroids,
one derived from each cluster. For a new sample, z, the
distance d(z) is calculated as the minimum distance to
each centroid. Then based on a user threshold, the clas-
sification decision is made. If d(z) is less than the
threshold the new sample belongs to the target class,
otherwise it is rejected.
2.6. One-Class Principal Component Analysis
Principal component analysis (PCA) is a classical statis-
tical method known as a linear transform that has been
widely used in data analysis and compression. Mainly
PCA is a projection method used for reducing dimen-
sionality in a given dataset by capturing the most vari-
ance by a few orthogonal subspaces called principal
components (PCs). For the one-class approach (OC-PCA)
one needs to build the PCA model based on the training
set and then for a given test example z the distance to the
PCA(z) model is calculated and used as a decision factor
for acceptance or rejection.
2.7. One-Class K-Nearest Neighbor (OC-KNN)
The one-class nearest neighbor classifier (OC-KNN) is a
modification of the known two-class nearest neighbor
classifier which learns from positive examples only. The
algorithm operates by storing all the training examples
as its model, then for a given test example z, the distance
to its nearest neighbor y(y=NN(z) ) is calculated as d(z,y).
The new sample belongs to the target class when:
yzd (2)
where NN(y) is the nearest neighbor of y, in other words,
it is the nearest neighbor of the nearest neighbor of z.
The default value of
is 1. The average distance of
the k nearest neighbors is considered for the OC-KNN
3.1. Naïve Bayes
Naïve Bayes is a classification model obtained by ap-
plying a relatively simple method to a training dataset
[28]. A Naïve Bayes classifier calculates the probability
that a given instance (example) belongs to a certain class.
It makes the simplifying assumption that the features
250 M. Yousef et al. / J. Biomedical Science and Engineering 3 (2010) 247-252
Copyright © 2010 SciRes. JBiSE
constituting the instance are conditionally independent
given the class. Given an example X, described by its fea-
ture vector (x1,...,xn), we are looking for a class C that
maximizes the likelihood: .
The (naïve) assumption of conditional independence
among the features, given the class, allows us to express
this conditional probability P(X | C) as a product of sim-
pler probabilities: .
(| )(,...,| )
) (|)
Px C
(|PX C
We used the Rainbow program [29] to train the naïve
Bayes classifier. To combine the numeric features identi-
fied in the miRNA-target duplex with the sequence fea-
tures (“words”) in the target candidate sequence, a dic-
tionary of all the unique “words” was generated and the
frequency of each “word” in the sequence is used.
3.2. Support Vector Machines (SVMs)
Support Vector Machines (SVMs) is a learning machine
developed by Vapnik [27]. The performance of this algo-
rithm, as compared to other algorithms, has proven to be
particularly useful for the analysis of various classifica-
tion problems, and has recently been widely used in the
bioinformatics field [30,31,32]. Linear SVMs are usually
defined as SVMs with linear kernel. The training data
for linear SVMs could be linear non-separable and then
soft-margin SVM could be applied. Linear SVM sepa-
rates the two classes in the training data by producing
the optimal separating hyper-plane with a maximal mar-
gin between the class 1 and class 2 samples. Given a
training set of labeled examples (, )
, i = 1,…,
where and
iRx 1,1{
R bR 
, the support vector
machines (SVMs) find the separating hyper-plane of the
form . Here, w is the “nor-
mal” of the hyper-plane. The constant b defines the posi-
tion of the hyper-plane in the space. One could use the
following formula as a predictor for a new instance:
wx b
xsignwxb for more information see Vapnik
3.3. Random Forest
Random forests are a ensemble of tree predictors such
that each tree depends on the values of a random vector
sampled independently and with the same distribution
for all trees in the forest [33]. The improvement in the
classification accuracy is due to the growing or an en-
semble of tress that vote for the most popular class.
Random forests are becoming increasingly popular be-
cause their ability to deal with small sample size with
high-dimensional space.
3.4. C4.5
C4.5 is a decision tree algorithm, developed by Quinlan
(1993)[34]. A decision tree is a simple structure where
non-terminal nodes represent tests on one or more at-
tributes and terminal nodes reflect decision outcomes.
4.1. Data
A collection of 326 confirmed MicroRNA targets (hu-
man, mouse, fruit fly worm and virus) were downloaded
from the TarBase [35] (TarBase_V4, Tarbase flat file
data as of 04/2007 ) web-site to serve as positive exam-
ples and 1,000 negative examples chosen at random
from the negative class pool generated at the study of
NBmiRTar [20].
To evaluate classification performance, we used the
data generated from the positive class and 1,000 nega-
tive examples. The negative class is not used for training
of the one-class classifiers, but merely for estimating the
specificity performance
Each one-class algorithm was trained using 90% of the
positive class and the remaining 10% was used for sensi-
tivity evaluation. The randomly selected 1,000 negative
examples were used for the evaluation of specificity. The
whole process was repeated 100 times in order to evaluate
the stability of the methods. Additionally, the Matthews
Correlation Coefficient (MCC) [36] measurement is used
to take into account both over-prediction and under-
prediction in imbalanced data sets. It is defined as:
MCC 
The one-class approach in machine learning has been
receiving more attention particularly for solving prob-
lems where the negative class is not well defined
[37,38,39,40]; moreover, the one class approach has
been successfully applied in various fields including text
mining [41], functional Magnetic Resonance Imaging
(fMRI) [42] ,signature verification [43] and miRNA
gene discovery [44].
This paper describes a comparison study of using one-
class and two-class approaches for miRNA target detec-
tion. The advantage of one class methods is that they
don’t require any additional effort for choosing the best
way of generating the negative class while it is clear that
the two class approaches performances are outperform
the one-class methods.
Table 1 shows the performance of five one-class clas-
sifiers while Table 2 shows the performances of two-
class methods. The results of the one-class approaches
show a slight superiority for OC-Kmeans over the other
one class methods based on the average of the MCC
measurement. The MCC measurement with value of +1
represents a perfect prediction while 0 value indicates an
average random prediction. However, accuracy is less
M. Yousef et al. / J. Biomedical Science and Engineering 3 (2010) 247-252
Copyright © 2010 SciRes.
Table 1. One-Class results.
0.69 0.81 0.779 0.91 OC-SVM
0.89 0.89 0.86 OC-Gaussian 0.75
0.87 0.87 0.90 OC-Kmeans 0.77
0.77 0.77 0.77 OC-PCA 0.55
0.89 0.89 0.87 OC-Knn 0.76
Tbale 2. Two class results.
Method TP TN Acc MCC
Naïve Bayes 0.93 0.99 0.99 0.93
SVM 0.98 0.9974 99.3 0.977
KNN4 0.858 0.952 92.88 0.813
C4.5 0.912 0.978 96.23 0.89
Random Forest 0.958 0.993 98.44 0.951
than the two-class approaches. During the training stage
of the one-class classifier we have set the 10% of the
positive data, whose likelihood is furthest from the true
positive data based on the distribution, as “outliers” in
order to produce a compact classifier. This factor might
cause a loss of information about the target class which
might also result in reducing performance compared to
the two class approach.
The current results show that it is possible to build up a
classifier based only on positive examples yielding a
reasonable performance. Moreover, more efforts are re-
quired to figure out more biological features to be used
in the design of the one-class classifier to improve the
performance. However, we hypothesize that taken 10%
of the training data as “outlier” is the cause of reducing
the one-class performance.
This project is funded in part under a grant with the ESHKOL scholar-
ship program of the ministry of science culture and sport to support
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