miRNA and Gene Expression based Cancer Classification using SelfLearning and Co-Training Approaches
Rania Ibrahim1, Noha A. Yousri2,1, Mohamed A. Ismail1 and Nagwa M. El-Makky1
rania.ibrahim.salama@gmail.com, noha.yousri@alexu.edu.eg, drmaismail@gmail.com and
nagwamakky@alexu.edu.eg
1
Computer and Systems Engineering Department, Alexandria University.
Alexanrdria 21544, Egypt.
2
Computer Science and Engineering, Egypt-Japan University of Science & Technology (E-JUST).

Abstract— miRNA and gene expression profiles have been
proved useful for classifying cancer samples. Efficient classifiers
have been recently sought and developed. A number of attempts
to classify cancer samples using miRNA/gene expression profiles
are known in literature. However, the use of semi-supervised
learning models have been used recently in bioinformatics, to
exploit the huge corpuses of publicly available sets. Using both
labeled and unlabeled sets to train sample classifiers, have not
been previously considered when gene and miRNA expression sets
are used. Moreover, there is a motivation to integrate both miRNA
and gene expression for a semi-supervised cancer classification as
that provides more information on the characteristics of cancer
samples.
In this paper, two semi-supervised machine learning
approaches, namely self-learning and co-training, are adapted to
enhance the quality of cancer sample classification. These
approaches exploit the huge public corpuses to enrich the training
data. In self-learning, miRNA and gene based classifiers are
enhanced independently. While in co-training, both miRNA and
gene expression profiles are used simultaneously to provide
different views of cancer samples. To our knowledge, it is the first
attempt to apply these learning approaches to cancer
classification. The approaches were evaluated using breast cancer,
hepatocellular carcinoma (HCC) and lung cancer expression sets.
Results show up to 20% improvement in F1-measure over
Random Forests and SVM classifiers. Co-Training also
outperforms Low Density Separation (LDS) approach by around
25% improvement in F1-measure in breast cancer.
Keywords— miRNA and gene expression analysis; Semisupervised Approaches; Self-Learning; Co-Training; Cancer sample
classifiers

I.

INTRODUCTION

MicroRNAs (miRNAs) are short (19–25 nucleotides)
noncoding single-stranded RNA molecules [1], which are
cleaved from 70–100 nucleotide miRNA precursors. miRNAs
regulate gene expression either at the transcriptional or
translational level, based on specific binding to the
complementary sequence in the coding or noncoding region of
mRNA transcripts [1]. Recent research has pointed out the
success of using miRNA and gene expression datasets in cancer
classification; miRNA profiles were used recently to
discriminate malignancies of the breast [2], lung ([2], [3]),

pancreas ([2], [4]) and liver ([5], [6], [7]). Enhancing the
accuracy of cancer classifiers, based on miRNA, gene, or mRNA
expressions, has been targeted in previous work ([8], [9], [10],
[11], [12], [13]). Moreover, different feature selections and
classification methods that efficiently detect the malignancy
status (normal or cancer) of the tissues were previously explored
in [11]. In addition, two classifiers are built [12], one for miRNA
data and another for mRNA data. The main drawbacks of the
approach is that it assumes paired miRNA and mRNA data for
each patient and it uses decision fusion rule to combine the
classifiers decision without enhancing the classifiers
themselves. Random Forests have been used in classifying
cancer in [14], [15] and [16]. Also, SVM has been used in
classifying cancer as in [17] and [18].
The idea of combining both labeled and unlabeled sets using
semi-supervised machine learning approaches has been used to
enhance classifiers in other domains like object detection [22],
word sense disambiguation [23] and subjective noun
identification [24]. Semi-supervised learning also has proved to
be effective in solving several biology problems like protein
classification [25] and prediction of factor-gene interaction [26].
However, in the field of sample classification using gene and
miRNA expression, semi-supervised machine learning
techniques were not considered before. Microarray experiments
are time consuming, expensive and limited, that is why usually
the number of samples of microarray-based studies is small [27].
Thus, huge publicly available gene/miRNA expression sets with
unlabeled samples are tempting to use for enriching training data
of sample classifiers. Integrating both miRNA and mRNA
expression profiles were thought to provide complementary
information [12], as miRNAs regulate gene expression at the
post-transcriptional level. In co-training, both miRNA and gene
expression profiles are used simultaneously to provide different
views of cancer samples. Semi-supervised machine learning
approaches are applied in this paper to discriminate cancer
subtypes. Discriminating cancer subtypes helps in
understanding the evolution of cancer and is used to find
appropriate therapies. For example, angiogenesis inhibitors like
bevacizumab are more effective in treating adenocarcinoma
lung cancer than squamous phenotypes ([19], [20]). Also, breast
cancer has an unpredictable response, and developing effective
therapies remain a major challenge in the clinical management
of breast cancer patients [21]. Moreover, identifying metastasis

hepatocellular carcinoma (HCC) samples is an important task as
metastasis is a complex process that involves multiple
alterations ([39], [40]).
In this paper, two semi-supervised machine learning
approaches, namely self-learning [28] and co-training ([29],
[30]) are used to enhance the classification accuracy of cancer
samples by combining both labeled and unlabeled miRNA and
gene expression profiles. In self-learning, a classifier is initially
constructed using the labeled set, then its accuracy is enhanced
by adding more data from unlabeled sets. Self-learning is used
on one particular source of expression, i.e either gene or miRNA
expression data. In co-training, two classifiers are trained, each
is specific to a different source of expression data (gene or
miRNA), termed as two views of the data. Based on the two
views, two classifiers are constructed and then used to train each
other. Exchanging information between the two classifiers
requires a mapping from miRNA expression to gene expression
or the opposite. A simple mapping is thus suggested based on
known biological relations between miRNAs and their target
genes.
The aforementioned classification approaches were
evaluated using gene and miRNA expression profiles of three
different cancer types: breast cancer, hepatocellular carcinoma
(HCC) and lung cancer. The results show around 20%
improvement in F1-measure in breast cancer, around 10%
improvement in precision in metastatic HCC cancer and 3%
improvement in F1-measure in squamous lung cancer over the
Random Forests and SVM classifiers. Also, the approaches were
compared to another semi-supervised approach called Low
Density Separation (LDS), which was used to enhance the
classifiers of cancer recurrence in [27]. The results show that cotraining outperforms LDS by exploiting the two different views,
i.e. miRNA expression view and gene expression view.
The paper is organized as follows section II discusses the
related work, while section III describes the proposed
approaches in details and section IV shows experimental results.
Finally section V concludes the paper.
II.

RELATED WORK

Using miRNA expression profiles to discriminate cancerous
samples from normal ones, and to classify cancer into its
subtypes, is an active research area and was applied to different
cancer types as breast [2], lung ([2], [3]), pancreatic in ([2], [4])
and liver in ([5], [6], [7]). The previous papers used one of the
following supervised machine learning techniques like SVM,
Prediction Analysis of Microarrays (PAM) and compound
covariate predictor.
Several attempts for enhancing cancer classifiers have been
recently introduced ([11], [12], [13]). In [11], number of feature
selection methods, as Pearson’s and Spearman’s correlations,
Euclidean distance, cosine coefficient, information gain and
mutual information and signal-to-noise ratio are used to enhance
cancer classifiers. Also different classification methods which
are k-nearest neighbor methods, multilayer perceptrons, and
support vector machines with linear kernel are used [11]. The
work has focused only on improving classifiers based on labeled
samples miRNA expression profiles and didn’t use publicity
available unlabeled sets, also, gene expression profiles were not

used to enhance miRNA based cancer samples classifiers.
Enhancing the classification accuracy by building two classifiers
one for miRNA data and another for mRNA data were explored
in [12]. That work first applies feature selection using relief-F
feature selection, then it uses bagged fuzzy KNN classifier and
finally it combines the two classifiers using fusion decision rule.
The drawback of the approach is that it assumes the existence of
both miRNA and mRNA data for each patient and it just uses
decision fusion rule to combine the classifiers decision without
enhancing the classifiers themselves. Another work [13] has
considered using discrete function learning (DFL) method on the
miRNA expression proﬁles to ﬁnd the subset of miRNAs that
shows strong distinction of expression levels in normal and
tumor tissues and then uses these miRNAs to build a classifier.
The paper didn’t combine multiple miRNA dataset or use gene
expression dataset to enhance the classifier. Semi-supervised
machine learning approaches were introduced in classification
using expression sets by using LDS approach which was used in
[27] to enhance cancer recurrence classifiers. Semi-supervised
machine learning approaches make use of the publicity available
unlabeled sets to enrich the training data of the classifiers.
However, the approach depends only on gene expression, and
didn’t combine both miRNA and gene expression sets.
Other semi-supervised machine learning approaches like
self-learning and co-training were introduced in other domains.
The heuristic approach of self-learning (also known as selftraining) is one of the oldest approaches in semi-supervised
learning and that was introduced in [28]. Self-learning was used
in many applications as object detection [22], word sense
disambiguation [23] and subjective noun identification [24].
Also, co-training is a semi-supervised approach that appeared in
[29] and [30] and is also used in applications as word sense
disambiguation [31] and email classification [32].
In this paper, self-learning and co-training approaches are
used. Both approaches use unlabeled sets to enhance classifiers
accuracy. Co-training also enhances the results by combining
both miRNA and gene expression sets. The results show
improvements over Random Forests and SVM classifiers and
LDS approach.
III.

SELF-LEARNING AND CO-TRAINING ADAPTATION TO
MIRNA/GENE BASED CLASSIFICATION

In self-learning and co-training, the objective is to construct
a classifier to discriminate between different cancer subtypes,
given the following:


The expression vector of a sample i, denoted xi, is
defined as follows:
𝑥𝑖 = {𝑒𝑖1 , 𝑒𝑖2 , … , 𝑒𝑖𝑗 , … , 𝑒𝑖𝑀 }
Where 𝑒𝑖𝑗 is the expression value of the jth
miRNA/gene, and M is the number of
miRNAs/genes.



N is the number of samples.

Two sets are used in both self-learning and co-training,
which are defined as follows:

b) Use the initial classifier to identify the subtype labels
of the unlabeled set U.
c)

Fig 1. Self-Learning approach overview.



A set of labeled samples L; 𝐿 = {𝑥𝑖 , 𝑦𝑖 }𝑁
𝑖=1
Where yi is the cancer subtype label.
A set of unlabeled samples U; 𝑈 =

{𝑥𝑖 }𝑁
𝑖=1

The size of U is expected to be much larger than L (|U| >>
|L|), which is expected to help enhancing the accuracy of the
classifiers by adding more expression vectors to the training
data. Increasing the number of unlabeled sets leads to higher
enrichment in the training set. Moreover, increasing the overlap
between the miRNAs/genes in the labeled and unlabeled sets
leads to increasing the effect of adding the unlabeled sets.
Self-learning [28] is a semi-supervised machine learning
approach, in which the labeled set L is used to build the initial
classifier and the unlabeled set U is utilized to enhance its
accuracy by adding the unlabeled samples with the highest
classification confidence to the training set, thus resulting in
making the classifier learns based on its own decision. Cotraining ([29], [30]) is also a semi-supervised machine learning
approach, which requires two views of the data. Two classifiers
are constructed separately for each view. Each classifier is then
used to train the other one by classifying unlabeled samples and
train the other view with the samples with highest classification
confidence.
The next sections explain how the two approaches are
adapted to use the unlabeled set U to enhance the baseline
classifier constructed based on L.
A. Self-Leanring Adaptation
The steps of adapting the self-learning approach are
described as follows:
a)

Train an initial cancer subtype classifier using set L.

Choose the most confident subset of cancer samples
(U'), i.e. samples classified with a confidence greater
than a given threshold (α).
d) Append the set of most confident samples to the initial
training dataset to form a new training set (U' ∪ L) for
re-training the classifier.
e) Use the classifier constructed at step d to perform
several iterations over the unlabeled set(s). At each
iteration, re-apply steps b, c and d.
The resulting classifier can then be used to classify new
samples based on their miRNA/gene expression profiles.
The confidence threshold α should be appropriately selected.
Decreasing α can increase the false positives rate. On the other
hand, increasing α can result in restricting the learning process
to the highly confident samples, typically the ones that are
most similar to the training data, thus losing the benefit of
including more training samples to the labeled data. Tuning
parameter α is thus important, since it affects the classifier’s
accuracy to choose the samples that will enhance the
classifier.
The next section explains the co-training idea and
adaptation in details.
B. Co-Training Adaptation
In this paper, the co-training approach is adapted to classify
cancer subtypes by training two different classifiers; the first is
based on the gene expression view and the second is based on
the miRNA expression view. Each view captures a different
perspective of the underlying biology of cancer and integrating
them using the co-training pipeline exploits this information
diversity to enhance the classification accuracy. The following
steps describes co-training in details:
a)

Two initial cancer classifiers are separately
constructed; one from the miRNA expression
dataset (LmiRNA) and another one from the gene
expression dataset (Lgene) using manually labeled
cancer subtypes sets.

b) Let the initial classifiers separately classify the
unlabeled cancer miRNA/gene expression datasets
(UmiRNA/ Ugene) into cancer subtypes.
c)

Choose the most confident labeled subtypes
samples (U'miRNA& U'gene) that have classification
scores greater than α.

d) Retrieve miRNA-gene relations using miRanda.
For the classifiers to train each other, miRNA
expression should be mapped to gene expression
and vice versa. miRNAs and their target genes
databases are used to map the datasets. In our case,
miRanda [33] database is used.
e)

Append the mapped miRNA expression sets to the
gene expression training sets and the mapped gene
expression sets to the miRNA expression training
sets and re-train the classifiers.

f)

Use the classifier constructed at step e to perform
several iterations over the unlabeled set(s). At each
iteration, re-apply steps b, c, d and e.

miRNA expression vector is constructed from its
target gene’s expression vector.. Due to the many
to many relationship between miRNAs and genes,
it is suggested to use an aggregation of all
expression vectors of the target genes to represent
the miRNA expression vector. Similarly, a gene
expression vector is constructed by aggregating the
expression vectors of the miRNAs that target this
gene. To map a gene to a miRNA, or the opposite,
it is proposed to take the mean expression value of
all miRNAs related to a gene, or the opposite, i.e.
the mean expression value of all genes related to a
miRNA. Experimental results show that taking the
mean value of expressions has improved the
classification accuracy. Part of the future work
would be investigating the effect of using other
methods as a mapping function.

g) In step d, a mapping between the miRNA view and
gene view is required. As shown in figure 2,
miRNAs and their target genes are related by a
many to many relationship; multiple miRNAs
target the same gene, and multiple genes are
targeted by the same miRNA. For the classifier to
exploit the two views, i.e. gene and miRNA sets, a

After the co-training process, the two classifiers can be
used independently, one on gene expression profile and the
other on miRNA expression profile of cancer samples. The
next section shows the experimental results of both selflearning and co-training approaches.
IV.

Fig 2. miRNAs and their target genes are related
by a many to many relationship. The first column
represents miRNAs and the second column
represents target genes ids.
Co-training ( LmiRNA, UmiRNA , Lgene , Ugene, α)
Inputs: miRNA expression profile of labeled set (LmiRNA),
miRNA expression profile of unlabeled sets (U miRNA ),
gene expression profile of labeled set (Lgene), gene
expression profile of unlabeled sets (Ugene) and confident
threshold (α)
Outputs: Two classifiers (CmiRNA) and (Cgene) that can
separately classify cancer samples.
Begin
Repeat {
1. CmiRNA = TrainClassifier (LmiRNA)
2. Cgene = TrainClassifier (Lgene)
3. L’miRNA = Classify (CmiRNA, UmiRNA)
4. LαmiRNA= ChooseMostConfident(L’miRNA, α)
5. L’gene = Classify (Cgene, Ugene).
6. Lαgene= ChooseMostConfident(L’gene, α).
7. LmiRNA = LmiRNA U ConverToMiRNAs(Lαgene)
8. Lgene = Lgene U ConverToGenes(LαmiRNA)
} Until (no improvement in classification
accuracy or reaching max iterations)
End
Fig 3. Pseudo code of co-training approach.

EXPERIMENTAL RESULTS

Two core classifiers of self-learning and co-training were
used, which are Random Forests and SVM. RF is a known
classifier ensemble method [34] based on constructing multiple
decision trees. Each decision tree is built on a bootstrap sample
of the training data using a randomly selected subset of features.
For predicting a sample's label, a majority vote based on the
classification obtained from the different decision trees is
calculated. RF have been used in classifying cancer in [14], [15]
and [16]. RF implementation from the Weka repository [35] was
used, and the number of decision trees was set to 10. SVM
implementation was also used from the Weka repository [35].
The approaches were evaluated using three cancer types,
namely breast cancer, hepatocellular carcinoma (HCC) and lung
cancer. miRNA based classifiers were constructed for breast
cancer and HCC sets, while gene based classifiers were
constructed for all 3 sets. In addition, self-learning and cotraining were compared against LDS in breast cancer and HCC.
LDS Matlab implementation was downloaded from [41]. Tables
1 and 2 show the size of the training and testing sets for each
cancer type according to its subtypes. All miRNA and gene
expression profiles were downloaded from NCBI [36].
Moreover, table 3 shows sample size and miRNA/gene numbers
in the unlabeled sets.
Table 1. Training and testing samples size for breast cancer
and HCC subtypes using miRNA expression. (NM = nonmetastatic and M = metastatic)
Type
Breast Cancer (GSE15885)
HCC
(GSE6857)
Subtype ER+/ ER-/
ER-/
ER+/
NM
M
Her2 Her2 Her2- Her2
+
+
Train
Test

8
7

2
2

5
4

1
0

193
162

62
65

30
25
20
15
10
5
0
Class 0

Class 1

Class 2

Initial RF Classifier

Self-Learning

Class 3

Co-Training

Fig 4. Training data size comparison of initial RF classifier against self-learning and co-training on breast cancer using miRNA
expression sets. (Class 0 is ER+/Her2-, class 1 is ER-/Her2+, class 2 is ER-/Her2- and class3 is ER+/Her2+)

Table 2. Training and testing sample size for breast cancer,
HCC and lung cancer subtypes using gene expression. (NM =
non-metastatic, M = metastatic, A = adenocarcinoma and S =
squamous)
Type
Sub
Type

Breast Cancer (GSE20713)
ER+/
ER-/
ER-/ ER+/
Her2
Her2
Her2
Her2
+
+

HCC
N
M
M

Lung
A
S

Train

17

9

14

4

98

118

87

41

Test

17

8

14

4

98

120

86

40

Table 3. Sample size and miRNA/gene numbers of unlabeled
sets.
Sample
Size

Table 4. Precision, recall and F1-measure for breast cancer
subtypes RF classifiers using miRNA expression and gene
expression dataset.

Baseline
classifier
Self-learning
iteration 1
Self-learning
iteration 2
Co-training

miRNAs based
classifier
P
R
F1
28.9
53.9
37.7

Genes based
classifier
P
R
F1
40.8
44.2 42.5

31.4

53.9

39.7

54.6

58.1

56.3

46.8

61.5

53.2

-

-

-

56.9

61.5

59.1

44.7

53.5

48.7

Table 5. Precision, recall and F1-measure for breast cancer
subtypes SVM classifiers using miRNA expression and gene
expression dataset.
miRNAs based classifier

miRNA/gene
Numbers

Breast Cancer (GSE26659)

94

237 miRNAs

Breast Cancer (GSE35412)

35

378 miRNAs

Breast Cancer (GSE16179)

19

54675 genes

Breast Cancer (GSE22865)

12

54675 genes

Breast Cancer (GSE32161)

6

54675 genes

HCC (GSE10694)

78

121 miRNAs

HCC (GSE15765)

90

42718 genes

Lung Cancer (GSE42127)

176

48803 genes

P
24.5

R
38.5

F1
29.9

LDS
Self-Learning

42.3
31.4

30.8
53.8

35.6
39.7

Co-Training

62.2

61.5

61.9

Baseline SVM
classifier

A. Breast Cancer
Breast cancer is a heterogeneous disease that has a range
of phenotypically distinct tumor types. This heterogeneity has
an underlying spectrum of molecular alterations and initiating
events that was found clinically through a diversity of disease
presentations and outcomes [21]. Due to the complexity of

this type of tumor, there is a demand for an efficient
classification of breast cancer samples.
For breast cancer, both self-learning and co-training are
used. Self-learning was applied for both miRNA and gene
based classifiers. For sample classification using miRNA
expression dataset, an initial breast cancer subtype labeled
dataset (GSE15885) was used to build an initial cancer
subtype classifier. The initial classifier was then used to
predict the labels of the unlabeled breast cancer subtypes
(GSE26659 and GSE35412).Two iterations were performed
over the two unlabeled datasets. The confident samples, the
ones with classification confidence (α) greater than 0.9 were
added to the training dataset and the subtype classifier was
re-trained. The same operation was repeated for sample
classification using gene expression dataset where the initial
dataset (GSE20713) was used to build an initial classifier
and the unknown subtype breast cancer (GSE16179) was
used to enrich it. Table 4 shows the precision, recall and F1measure enhancement against the RF classifier. The results
show 12% improvement in F1-measure of breast cancer
subtype classifier using miRNA expression profiles and 6%
improvement in F1-measure of breast cancer subtype
classifier using gene expression profiles. Moreover, table 5
shows the enhancement over SVM and LDS classifiers, only
miRNA expression profiles were used in this comparison as
LDS requires a lot of memory and thus was unable to use
with large number of genes. The table shows that selflearning achieved 10% improvement in F1-measure over
SVM classifier and 4% improvement in F1-measure over
LDS classifier.
Co-training was evaluated in breast cancer subtypes in
both miRNA expression and gene expression. To enhance
sample classification using miRNA expression, one labeled
miRNA expression dataset (GSE15885) is used. One labeled
gene expression dataset (GSE20713) and three unlabeled
gene expression datasets (GSE16179, GSE22865 and
GSE32161) are mapped into miRNA expression values (as
explained in subsection B of section III). In addition, to
enhance sample classification using gene expression, one
labeled gene expression dataset (GSE20713) is used. One
labeled miRNA expression dataset (GSE15885) and two
unlabeled miRNA expression datasets (GSE26659 and
GSE35412) are mapped into gene expression values and
added to the gene training dataset. Table 4 shows the
significant improvements in F1-measure using co- training
over RF classifier. Increments up to 21% and 8% in F1measure are observed when using miRNA expression
profiles and gene expression profiles respectively.
Moreover, table 5 shows the enhancement of co-training
over SVM and LDS classifiers, co-training was able to
enhance the F1-measure by around 25% over the LDS
classifier.
To have a closer look on the behavior of the methods,
the number of training data at each class is determined and
shown at figure 4. The figure shows that co-training was able
to enrich the training data in all 4 classes which is reflected
in the highest improvement in the results and self- learning
was able to enrich that training set in class 0.

B. HCC
Hepatocellular carcinoma (HCC) represents an
extremely poor prognostic cancer that remains one of the
most common and aggressive human malignancies
worldwide ([37], [38]). Metastasis is a complex process that
involves multiple alterations ([39], [40]), that is why
discriminating metastasis and non-metastasis HCC is a
challenging problem.
For HCC, both self-learning and co-training approaches
were evaluated to discriminate between metastatic and nonmetastatic HCC. The self-learning steps are applied using
GSE6857 as an initial labeled miRNA expression dataset
and GSE10694 as the unlabeled subtypes HCC samples.
Also, GSE36376 was used as initial labeled gene expression
datasets and GSE15765 as the unlabeled subtypes HCC
samples. For co-training, to enhance sample subtype
classifier using miRNA expression, one labeled miRNA
expression dataset (GSE6857) is used. One labeled gene
expression dataset (GSE36376) and one unlabeled gene
expression datasets (GSE15765) are mapped into miRNA
expression values and added to the miRNA training datasets
and the sample subtype classifiers are re-trained. Also, in
order to enhance the sample classification using gene
expression, one labeled gene expression dataset (GSE36376)
is used. One labeled miRNA expression dataset (GSE6857)
and one unlabeled miRNA expression datasets (GSE10694)
are mapped into gene expression datasets and added to the
gene training dataset.
Table 6 shows detailed results for HCC subtype
classification using RF core classifier, there is around 10%
improvement in precision of HCC metastasis class using
miRNA expression sets and around 2% in F1-measure using
gene expression sets. Moreover, table 7 shows the
improvement of the techniques over SVM and LDS
classifiers. Co-training achieved 5% enhancement in recall
over SVM classifier and 6% enhancement in F1-measure
over LDS classifier. The improvement in HCC is less than
breast cancer as in breast cancer the number of used
unlabeled sets are larger. Also, the overlapping between the
miRNAs and genes between the initial set and the added sets
is an important factor. In order to understand why
enhancements in breast cancer were more significant, the
number of overlapping miRNAs and genes is calculated.
Tables 8 and 9 show that the higher the overlap between the
miRNAs and genes of the initial set and those of the added
sets, the higher the improvements become.
C. Lung Cancer
Lung cancer is the leading cause of cancer-related death in
both men and women worldwide, it results in over 160,000
deaths annually [8]. Only self-learning using gene
expression dataset was evaluated in lung cancer as no labeled
miRNA expression dataset was found on the web. The aim
of the cancer subtype classifier is to discriminate between
adenocarcinoma and squamous lung cancer subtypes. The
labeled gene expression dataset (GSE41271) was used to
build an initial classifier and the unlabeled gene expression
dataset (GSE42127) was used to enhance it. Table 10 shows
the enhancement achieved by self-learning, which is around

3% improvement in F1-measure of squamous lung cancer
class.

Table 10. Results of lung cancer RF subtypes classifiers using
gene expression dataset.
Class A

Table 6. Results of HCC RF subtype classifiers using
miRNA/gene expression dataset.
Class NM

miRNA
initial
classifie
r
miRNA
selflearning
miRNA
cotraining
Genes
initial
classifie
r
Genes
selflearning

Class M

P
75

R
93

F1
83

P
59

R
24

F1
34

Weighted
Evaluation
P
R
F1
70
73 72

76

95

84

69

24

36

74

75

Genes
initial
classifier
Genes
selflearning

Class S

P
83

R
95

F1
89

P
83

R
54

F1
65

Weighted
Evaluation
P
R
F1
83
83 83

84

96

90

87

56

68

85

76.6

95

84.9

69

27

39

74

75

75

95

98

96

98

95

97

96

96

96

100

96

98

97

100

98

98

98

98

R
66.1

F1
66.3

LDS

61.2

66.9

63.9

Self-Learning

62.2

61.5

61.9

Co-Training

67.7

71.4

69.5

Table 8. The number of overlapping miRNAs and genes
between initial datasets and added datasets in breast cancer.
GSE15885
GSE26659
GSE35412
GSE20713
GSE16179

miRNAs initial dataset
(GSE15885)
336
124
183
157
157

Genes initial dataset
(GSE20713)
7
7
7
54676
54676

Table 9. The number of overlapping miRNAs and genes
between initial datasets and added datasets in HCC.
GSE10694
GSE36376
GSE15765

miRNAs initial dataset
(GSE6857)
36
52
52

Genes initial dataset
(GSE36376)
47323
37282

CONCLUSION

In this paper, two semi-supervised machine learning
approaches were adapted to classify cancer subtype based on
miRNA and gene expression profiles. They both exploit the
expression profiles of unlabeled samples to enrich the
training data. The miRNA-gene relation is additionally used
to enhance the classification in co-training. Both selflearning and co-training approaches improved the accuracy
compared to Random Forests and SVM as baseline
classifiers. The results show up to 20% improvement in F1measure in breast cancer, 10% improvement in precision in
metastatic HCC cancer and 3% improvement in F1-measure
in squamous lung cancer. Co-Training also outperforms Low
Density Separation (LDS) approach by around 25%
improvement in F1-measure in breast cancer.

Table 7. Results of HCC subtype SVM classifiers using
miRNA expression dataset.
P
66.5

85

74

V.

Baseline SVM
classifier

85

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