MicroRNA Prioritization based on Target Proﬁle Similarities

P´eter Marx

1,3

, Bence Bolg´ar

, Andr´as G´ezsi

, Attila Guly´as-Kov´acs

and P´eter Antal

Department of Measurement and Information Systems,

Budapest University of Technology and Economics, Budapest, Hungary

Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary

MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Budapest, Hungary

Keywords:

microRNA, microRNA Target, Kernel Methods, Multiple Kernel Learning, Gene Prioritization.

Abstract:

microRNAs form a complex regulatory network with thousands of target genes. This network is known to

suffer speciﬁc, but largely elusive, genetic perturbations in various types of disease. Accurate prioritization

of microRNAs for each disease type would elucidate those perturbations and so facilitate therapeutic and

diagnostic design. The multiple target proﬁles of microRNAs stemming from various experimental and in

silico methods allow the deﬁnition of wide range of similarities over microRNAs, but the combined use of

these of heterogeneous similarities was not utilized in the gene prioritization approach. Using microRNAs

as bases, prioritization with a disease-speciﬁc query set of microRNAs is straightforward once a microRNA-

microRNA similarity matrices have been derived. Here we demonstrate the application of a one-class version

of the multiple kernel learning framework in order to fuse heterogeneous characteristics of microRNAs. We

evaluate the method with breast cancer-speciﬁc queries, illustrate its technological aspects, and validate our

results not only by standard leave-one-out cross validation, but also with a prospective evaluation.

1 INTRODUCTION

The growing availability of omic measurements and

multiple characterizations of entities like genes or

proteins led to the emergence of data and knowl-

edge fusion as a central challenge in various ﬁelds.

From the 90’s, the similarity based virtual screen-

ing became a popular method in chemoinformatics,

relying mostly on rank fusion and heuristic fusion

of data sources (Johnson and Maggiori, 1990; Ginn

et al., 1997; Ginn et al., 2000; Eckert and Bajorath,

2007). The aim of these methods were to make pre-

dictions based on different heterogeneousinformation

sources in various research ﬁelds. From the 2000’s,

gene prioritization emerged as a separate task with

the fusion of wide range of information about genes

and gene products, such as sequence similarities,

transcriptional regulation similarities or expression

level similarities (Freudenberg and Propping, 2002;

Aerts et al., 2006; Kohler et al., 2008; Moreau and

Tranchevent, 2012). A theoretically sound foundation

for omic data fusion using similarities was proposed

in 2004 by Lanckriet et al., which utilized the kernel

methods for large-scale similarity integration (Lanck-

riet et al., 2004). Currently, wide range of methods

were reported for data and knowledge fusion, such

as the aforementioned kernel-based methods and the

more traditional text mining and network based fusion

methods (De Bie et al., 2007; Liekens et al., 2011; Lee

et al., 2011). Although there are many open questions,

such as the management of systematically incomplete

similarities, uncertainty in similarities, biological rel-

evance of ranks and proper evaluation, the kernel-

based fusion methods allows a universal framework

for any set of entities and it achieved an impressive

performance in multiple settings and domains, com-

pared to for example rank fusion (Bornigen et al.,

2012; Arany et al., 2013). Despite the universality

of the kernel-based fusion and prioritization frame-

work, the basis for the applications so far were nearly

exclusively gene centric, with some exceptions such

as its application for protein-ligand interaction pre-

diction (Iacucci et al., 2012) and for drug reposition-

ing (Arany et al., 2013; Bolg´ar et al., 2013). In this

paper we report the application of the kernel-based

fusion and prioritization framework over miRNAs, as

a step towards the extension of this methodology to

integrate information over heterogeneous sets of enti-

ties.

MiRNAs are short approximately 22 base pairs

278

Marx P., Bolgár B., Gézsi A., Gulyás-Kovács A. and Antal P..

MicroRNA Prioritization based on Target Proﬁle Similarities.

DOI: 10.5220/0004925502780285

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2014), pages 278-285

ISBN: 978-989-758-012-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

long non-coding RNAs and have an important role in

cis-regulation. Generally miRNAs recognize their tar-

get by the 2-8 bps long seed region at the 5’ end of the

miRNA. They bind to the 3’ end of the target mRNA

by Watson-Crick base pairing and can repress the

mRNA translation in multiple ways such as mRNA

degradation, mRNA de-deadenylation, inhibition of

initiation and premature ribosome drop off. MiRNAs

can also have binding sites in the coding region or at

the 5’ end of the targetmoreoverone miRNA can have

multiple targets and one target can bind to multiple

miRNAs. Although there is an exponentially increas-

ing interest towards miRNAs (more than 2000 miR-

NAs are known in human cells), plenty of their target

genes are only connected to them based on in silico

prediction. There is a wide variety of in silico meth-

ods for target exploration, prediction and validation,

but these methods are somewhat complementary and

suffer from different biases. To avoid these errors we

used only experimentally supported data in this paper.

For additional information on miRNAs and gene reg-

ulation see (Nilsen, 2007) and (Chen and Rajewsky,

2007).

Many studies suggested an important role for

miRNAs in the development of cancer based on the

observation that their expression levels changed in tu-

mor tissues which led to altered mRNA quantity in-

side the cell, and similarly both inherited and somatic

genetic variations related to miRNAs were reported as

associated with diseases. In the paper we illustrate the

new miRNA-based prioritization method with gen-

eral, disease aspeciﬁc kernels and with breast cancer

queries. Breast cancer is one of the leading cancer

types among the female population, and both miRNA

related genetic variations and expression levels were

reported to be associated with various phenotypic fea-

tures of breast cancer classes.

Our goal is to examine the efﬁciency of miRNA

prioritization based on the already available data

which could extend the current gene centric prioriti-

zation. Predicting new miRNA-disease connections

can lead to a more detailed description of the regula-

tion of the disease in focus. Since the miRNA data is

coming from different experiments and including in

silico target predictions would increase heterogeneity

we use a well known information fusion method as

well and compare it to the other one kernel prioriti-

zation algorithm. For evaluation of the performance

of the prioritization we apply the leave-one-out cross-

validation and because of its potential positive bias

through knowledge contamination, see e.g. (Kohler

et al., 2008; Bornigen et al., 2012), we performed a

prospective evaluation using a recent summary from

Jacobsen et al. (Jacobsen et al., 2013).

2 EARLIER WORKS

The data fusion based prioritization methods through

integration of similarities and rankings from multi-

ple information source has a long and somewhat re-

dundant history, with its roots in virtual screening

in chemoinformatics (Johnson and Maggiori, 1990;

Ginn et al., 1997; Ginn et al., 2000; Eckert and

Bajorath, 2007), kernel methods in chemoinformat-

ics (Burbidge et al., 2001; Warmuth et al., 2003), in

the one-class classiﬁcation and prioritization (Moya

and Hush, 1996; Schlkopf et al., 2001), in multi-

ple kernel learning (Lanckriet et al., 2004; Rako-

tomamonjy et al., 2008) and in gene prioritization

methods (Freudenberg and Propping, 2002; Aerts

et al., 2006; Kohler et al., 2008; De Bie et al., 2007;

Liekens et al., 2011; Lee et al., 2011; Moreau and

Tranchevent, 2012).

Compared to the growing admittance of the cen-

tral role of miRNAs, the use of this information

source is surprisingly rare. For example, transcrip-

tion regulation based gene-gene similarities (kernels)

are dominantly constructed base on the commonality

of the transcription factor binding sites of the gene

pairs, and not by the commonality of miRNAs (see

e.g. (Aerts et al., 2006)). Two notable exceptions re-

ported the prioritization of miRNAs using a network

approach (Xu et al., 2011) and using a support vector

machine classiﬁer (Li et al., 2012). The work reported

in this paper continues this line as it uses miRNAs for

the bases of prioritization, instead of the prevailing

gene centric approach. However, this work adopts a

more principled approach for fusion by adopting the

multiple kernel learning framework in the one-class

classiﬁcation (/prioritization) settings.

3 METHODS

The number of databases containing miRNA-target

pairs is growing just like the methods which utilize

machine learning algorithms to ﬁnd further potential

targets for a miRNA (Ficarra et al., 2012). However

applying such methods largely increase the number

of miRNA-target pairs and extend the miRNA set us-

ing predicted connections, it leads to higher degree of

uncertainty at the results. Therefore we used only ex-

perimentally supported targets for every miRNA. Us-

ing the predictions based results requires an extensive

evaluation of the methods which will be aimed in a

future study.

MicroRNAPrioritizationbasedonTargetProfileSimilarities

279

3.1 Computing miRNA Kernel

We used miRTarBase (Hsu et al., 2011) (downloaded

11/14/2013 Release: 4.5) to collect experimentally

validated miRNA-target pairs. We ﬁltered out the

non-human target genes from the data resulting in a

dataset of 596 unique miRNAs and their experimen-

tally supported targets. Similarity between two miR-

NAs is deﬁned by the number of common target genes

in a given context, e.g. using a given miRNA target

validation method. The primary diagonal of the ma-

trix X contains the degree of a miRNA which equals

with the number of targets in the present study. To

extend our deﬁnition of miRNA similarity and to en-

sure positive deﬁniteness we squared the matrix and

normalized it with a diagonal matrix D containing the

reciprocal of the square root of the diagonal elements

of X.

K = DX

This way two miRNAs i, j will be more similar (x

will be higher) if they share more targets or they have

common similar miRNAs. miRTarBase contains in-

formation on the experimental methods used to vali-

date the miRNA-target pairs. These methods can be

classiﬁed into two groups based on the strength of the

evidence they provide. E.g. microarrays give indi-

rect proof as the prediction is based on the differential

expression miRNA-gene pairs therefore it is a ’weak’

validation. On the contrary attaching reporter genes

to the target gene proves directly the miRNA effects

on the gene (Ficarra et al., 2012). We used the classi-

ﬁcation of miRTarBase (functional, functional-weak,

nonfunctional, nonfunctional-weak) and built differ-

ent kernels for each of them with the above described

method.

3.2 Multiple Kernel Learning

The most important beneﬁt of Multiple Kernel Learn-

ing is that it provides a way to perform both data fu-

sion and prioritization in a joint manner, by solving

one single optimization problem. This is achieved

by incorporating kernel “weights” into the objec-

tive function, which essentially amounts to determin-

ing an optimal weighting of (possibly very heteroge-

neous) information sources by exploiting the infor-

mation content of the query itself. Further favorable

properties include computational efﬁciency, ﬂexibil-

ity (depending solely on pairwise similarities), and

often very good empirical performance. Multiple ker-

nel methods have already been successfully applied in

the context of genomic data fusion (Lanckriet et al.,

2004), however, ﬁrst formulations did not support

gene prioritization. The ﬁrst tool which performed

fusion and prioritization in a coupled way involved a

modiﬁcation of the multiple kernel one-class SVM al-

gorithm (Sch¨olkopf et al., 2001) to perform the rank-

ing of the entities based on their projections to the

normal of the hyperplane (De Bie et al., 2007). In

order to attain better computational performance, we

modiﬁed the formulation of Vishwanathan et al. (Sun

et al., 2010):

min

f,ρ,ξ

ξ,d

∑

k f

− ρ+

νl

∑

s.t.

∑

) ≥ ρ− ξ

ξ ≥ 0, d

d ≥ 0, i = 1, 2, . . . , l,

where f

f parameterizes the hyperplane, d

is the

weight of the kth kernel, ρ denotes the margin, ν con-

trols the model complexity, ξ

ξ are the slack variables

and the last term stands for the Lp-norm regulariza-

tion of the kernel weights. Note that this is just the

primal of the one-class SVM augmented by the ker-

nel weights.

In view of earlier ﬁndings (Yu et al., 2010), we

utilized the squared L2-norm as weight regularizer.

In this setting, the dual reads

max

−

8λ

∑





s.t. 0 ≤ α

≤ 1, 1

α = νl,

where α

α are the dual variables and the optimal ker-

nel weights can be recovered from the solution. Note

that this dual is differentiable and convex, which is

very easy to optimize. The score of a sample can be

computed as

score(x

x) =

∑

, x

∑

i.e. here we determine the distance to the hyperplane

instead of the side on which the sample x

x lies; samples

are then ranked on the basis of their respective scores.

4 RESULTS

We used multiple kernels and training sets (queries)

to cover the different subtypes of breast cancer clas-

siﬁed by Samantarrai et al. (Samantarrai et al., 2013)

as follows: Ductal carcinoma in situ, Lobular carci-

noma in situ, Invasive ductal- or invasive lobular car-

cinoma. Our ﬁrst approach was to collect a subset

of miRNAs from one subtype of breast cancer and

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280

1 5 20 100 500

0.0 0.2 0.4 0.6 0.8

Entities

Scores

Figure 1: The scores of the miRNAs for the MKL kernel

and the third query (axis X is log scaled).

one type of differential expression (up- or downreg-

ulated). We will refer to these subset of miRNAs as

a class. This way the prioritization should recover

the remaining set of miRNAs of the same class, and

it should give a higher score to other miRNAs con-

nected with breast cancer. The other approach was

to choose miRNAs which are present in more sub-

classes to catch the different properties of each sub-

type and prioritize with this breast cancer “hyper-

class”. In both cases we prioritize with the MKL

algorithm and also the kernel built from the full hu-

man miRTarBase. The ﬁrst query (Query1) contained

the hsa-miR-182, hsa-miR-183, hsa-miR-200c, hsa-

miR-21. The second query (Query2) contained miR-

NAs of the subtype ductal carcinoma in situ downreg-

ulated miRNAs (hsa-miR-125b, hsa-miR-127, hsa-

miR-210, hsa-miR-7). We included those miRNAs in

the last query (Query3) which were present in more

classes to span the set of miRNAs which are con-

nected to the disease. These are hsa-let-7d, hsa-miR-

182, hsa-miR-183, hsa-miR-21, hsa-miR-210, hsa-

miR-221. When it was not speciﬁed and for one pre-

miRNA more mature miRNA were available we used

both the 3p and 5p miRNA. We analyzed other queries

also which gave similar results and for this reason

those are not included in the manuscript.

Query1 with the full kernel ranked the other miR-

NAs in the same class hsa-miR-361-5p, hsa-miR-

374a, hsa-miR-93 113th, 52th, 12th respectively and

resulted an average 126.46 rank for the all miRNAs

whereas the MKL kernel ranked them 29th, 84th, 13th

with an average of 121.96. Both methods ranked

miRNAs from other classes in the ﬁrst 10% of the

Table 1: Leave-one-out cross validation results for the duc-

tal carcinoma in situ breast cancer type upregulated miR-

NAs. The numbers indicate the rank of the left out miRNA.

microRNA Full MKL

hsa-miR-21 169 113

hsa-miR-200c 257 57

hsa-miR-182 41 31

hsa-miR-183 60 54

hsa-miR-361-5p 101 28

hsa-miR-374a 63 119

hsa-miR-93 16 14

Average 101 59.43

data. The rank of the miRNA is in brackets. Full:

hsa-miR-221 (8), hsa-miR-96 (44); MKL: hsa-miR-

221 (15), hsa-miR-96 (42), hsa-miR-18 (44). Query2

ranked the miRNAs from the same class (ﬁrst num-

ber belongs to the full kernel and the second to the

MKL kernel set) hsa-let-7d (222,123), hsa-miR-221

(88,60), hsa-miR-320 (23,66) and others full: hsa-

miR-182 (51), hsa-miR-361-5p (43), hsa-miR-9 (50),

hsa-miR-93 (20) and MKL: hsa-miR-182 (25), hsa-

miR-361-5p (33), hsa-miR-9 (41), hsa-miR-93 (38),

hsa-miR-10b (40) and hsa-miR-21 (57). The average

ranks of miRNAs from all subtypes are 120.83 for the

full kernel and 112.92 for the MKL set. The results

for the last query is similar to the ﬁrst two, generally

using MKL kernel set givesa lower average rank. Fig-

ure 1 shows the scores of the last query for the MKL

kernel set on a log scale. The cut-off point shows a

break between the query elements and the other miR-

NAs. The MKL method computes a weight for every

kernel. In our case these weights were almost uniform

and not varied between the different queries. Gener-

ally, the weak functional set had 0.4 weight and the

other kernels 0.2.

For validation we used not only the same set of

miRNA, but also the results from Jacobsen et al.

(Jacobsen et al., 2013) for a prospective evaluation.

They computed the correlation R

between miRNAs

expression levels and DNA copy number variation.

Based on their results the variation of the expression

levels in case of cancer related miRNAs can be ex-

plained by DNA copy number variation. We selected

the miRNAs which has a higher than 0.1 correlation

and which are known to play a role in breast can-

cer. This routine makes it possible to validate our

results against a newer breast cancer dataset (see Ta-

ble 2). Furthermore, leave-one-out cross validation

(LOOCV) was performed on the Ductal carcinoma in

situ upregulated miRNA set. The average rank of the

left out miRNA was 101 with the full kernel and 59.43

with the MKL algorithm (see Table 1).

MicroRNAPrioritizationbasedonTargetProfileSimilarities

281

Figure 2: The graph of the ﬁrst 50 ranked miRNAs for the full kernel and the ﬁrst query. Only edges with higher than 0.4

similarity scores are shown. The color of a node is deﬁned by its score, where blue indicates lower score.

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Table 2: The miRNAs ranked below 50 from the Jacobsen

dataset. *The miRNA was part of the query.

microRNA

Query1 Query3

Full MKL Full MKL

hsa-miR-106b 11 51 12 79

hsa-miR-141 51 27 375 386

hsa-miR-15b 26 11 47 30

hsa-miR-17 41 20 85 41

hsa-miR-19b 13 24 34 36

hsa-miR-200c 6* 5* 280 95

hsa-miR-20a 43 28 92 88

hsa-miR-21 2* 2* 14* 3*

hsa-miR-92a 16 31 3 18

hsa-miR-140 61 22 121 80

hsa-miR-182 3* 6* 16* 2*

hsa-miR-183 1* 1* 15* 1*

hsa-miR-186 76 18 68 34

hsa-miR-25 10 25 9 37

hsa-miR-320a 107 87 32 43

5 DISCUSSION

Despite the popularity of gene prioritization, there are

only a few studies in which researchers used miRNA

prioritization for ﬁnding novel disease related miR-

NAs. To our knowledge, those are only validated by

LOOCV. The quantitative evaluation of the perfor-

mance of our method for the queries (Query 1,2,3)

shows that it ranks most of the miRNAs related to

breast cancer according to the review from Samantar-

rai et al. (Samantarrai et al., 2013) in the ﬁrst 20%

of the miRNA set used in our study. Bornigen et

al. (Bornigen et al., 2012) used the top 5 %, 10 %,

30 % to validate the performance of the prioritiza-

tion methods for real examples. Taking the number

of miRNAs into account this rate (top 20% includes

almost all miRNAs related to breast cancer) can be

enough for experimental validation with a real situa-

tion with limited budget also.

On Figure 2 miRNAs are connected based on the

similarity of miRNA-miRNA pairs. Nodes even with

lower rank has plenty of neighbors, on the other hand

nodes with higher scores, typically query members,

share less properties with the other miRNAs in the

graph (i.e. connected with edge above the threshold

0.4). The manual evaluation of the graph also sug-

gested that the prioritization gives higher score not

only for those miRNAs which are in the same miRNA

cluster as the query miRNAs. Generally the nodes

with more connections belongs to a miRNA family

represented by more members in the ﬁrst 50 ranked

entities, which veriﬁes our similarity deﬁnition.

The prospective validation on the results of Ja-

cobsen et al. ranked the miRNAs in Table 2 on

the ﬁrst 10% of the whole set. This means that

the applied method was able to identify new can-

didates which were experimentally validated later

without retrospective bias. The role of these miR-

NAs in cancer development is supported by exper-

imental evidence. For example miR-17 and miR-

200c are oncosuppressors and miR-92 is an oncomiR

(Samantarrai et al., 2013). Furthermore (Kim et al.,

2011) found inverse correlation between hsa-miR-17,

hsa-miR-92, hsa-miR-106b, hsa-miR-20a, hsa-miR-

19 and ZBTB4 gene which has a signiﬁcant corre-

lation to relapse-free survival. The almost uniform

kernel weights most likely caused by the similar in-

put data as for plenty of miRNA-target pairs are sup-

ported with more than one experimental evidence.

The higher weight of weak functional kernel is pos-

sibly the consequence of the higher number of con-

nections available. Our results show that the MKL

method which includes both prioritization and fusion

outperformed the single kernel prioritization in all of

the above cases. With even more heterogeneous in-

formation source the application of the MKL method

can be even more advantageous.

6 CONCLUSIONS

The high heterogeneity of experimental and in sil-

ico methods for miRNA target exploration, prediction

and validation result in highly heterogeneous miRNA

proﬁle and respectively, miRNA similarities. We ap-

plied the multiple kernel learning framework for the

fusion of these similarities in breast cancer. Results

indicate that this methodology can be a promising ap-

proach to ﬁnd new miRNAs related to cancer.

Furthermore, this approach could be used to re-

ﬁne existing miRNA families and to deﬁne more de-

tailed gene-gene similarities . Our results also indi-

cate that miRNA prioritization can support study de-

sign and interpretation of miRNA discovery, and it

also indicates the universal applicability of kernel-

based fusion methods over different sets of entities,

such as miRNAs, beside the current gene centric ap-

proach. With the growing number of discovered miR-

NAs and the deeper understanding of their role in the

post-transcriptional regulation miRNA prioritization

can lead to a more detailed description of miRNA-

gene regulatory networks.

However, the approach described in this paper

MicroRNAPrioritizationbasedonTargetProfileSimilarities

283

seems to be successfully identify even novel miR-

NAs for breast cancer still there are many question

to be answered. MiRNA networks or miRNA simi-

larity can be deﬁned in other ways such as comparing

the sequence or based on single nucleotide polymor-

phisms (SNP) or by using conservation scores. After

a thorough investigation of the target prediction algo-

rithms we can include predicted miRNA-target pairs

in the analysis to increase the number of miRNAs in

the study. Last with the growing number of avail-

able data it will be possible soon to build more tissue

speciﬁc miRNA networks. This way we will have a

heterogeneous data source and we can make a good

use of the MKL method as it can be used to com-

pare the importance of the different similarity scores

or data sources based on the learned weights of the

kernels. Future work will concentrate on answering

these questions.

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