http://www.iaeme.com/IJARET/index.asp 141 [email protected]
International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 9, September 2020, pp. 141-155, Article ID: IJARET_11_09_015
Available online at http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=9
ISSN Print: 0976-6480 and ISSN Online: 0976-6499
DOI: 10.34218/IJARET.11.9.2020.015
© IAEME Publication Scopus Indexed
MODIFIED WEIGHTED SIMILARITY IN
HETEROGENEOUS GRAPH FOR PREDICTION
OF miRNA DISEASE ASSOCIATION
Rashmi J R
Research Scholar, Department of Studies in Computer Science,
University of Mysore, Mysore, India
Lalitha Rangarajan
Retired Professor, Department of Studies in Computer Science,
University of Mysore, Mysore, India
ABSTRACT
In the last decade lot of experimental research has witnessed and verified the
important roles of miRNA in the development of complex human diseases. Publicly
available MiRNA data and different analysis methodologies have given rise to the
development of many computational models to predict miRNA disease association.
Predicting accurate miRNA disease association is very essential for the proper
diagnosis and treatment of diseases. During past few years lots of computational
methods have been developed. However, each method has its own limitation and it has
not yet been possible to develop an efficient method that can predict miRNA-disease
associations accurately. In this paper, weighted meta-graph based computational
approach for predicting the association between diseases and miRNAs is proposed.
The proposed algorithm is designed by integrating available miRNA functional
similarity, miRNA similarity based on Environmental factors, miRNA similarity based
on diseases to get the average miRNA similarity, disease semantic similarity and
disease functional similarity are also integrated to get the average disease similarity.
AUC of 0.9617833 on global LOOCV has been achieved using the proposed method.
Key words: Meta-graph, MiRNA functional Similarity, Disease Semantic Similarity,
Heterogeneous Information Network
Cite this Article: Rashmi J R and Lalitha Rangarajan, Modified Weighted Similarity
in Heterogeneous Graph for Prediction of Mirna Disease Association, International
Journal of Advanced Research in Engineering and Technology, 11(9), 2020,
pp. 141-155.
http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=9
Modified Weighted Similarity in Heterogeneous Graph for Prediction of Mirna Disease
Association
http://www.iaeme.com/IJARET/index.asp 142 [email protected]
1. INTRODUCTIONS
MicroRNAs or MiRNA are short non-coding RNA molecules that are involved in different
physiological and developmental processes by controlling the gene expression of target
mRNAs. They play important roles in almost all kinds of cancer where they modulate key
processes during tumor genesis such as metastasis, Apoptosis, proliferation, or angiogenesis
[11]. Depending on the mRNA targets they regulate, they can act as oncogenes or as tumor
suppressor genes. Multiple links between microRNA biogenesis and cancer highlight its
significance for tumor diseases. However, mechanisms of their own regulation on the
transcriptional and posttranscriptional level in health and disease are only beginning to
emerge [11]. MiRNA disease association prediction is one of the emerging and essential
fields of research.
1.1. Background
It has been verified that miRNAs are the positive regulators. Experimental approaches for
identifying the miRNA disease associations are having high precision but they are considered
as time consuming and expensive. In the past few years many experiments have been
conducted lot of data is being collected and stored in the databases such as dbDEMC [5] and
HMDD [6] which contains human miRNA diseases which are experimentally verified. Still
the research in the field of miRNA disease association is ongoing; in order to complement the
biologists in their work many computational approaches have been proposed. In recent years
many computational approaches have given outstanding performance such as most of the
methods are based on the assumption that similar miRNAs are tend to associated with the
similar diseases. Chen et al [1] proposed the RWRMDA to predict disease associated
miRNAs. In this method the global similarity for miRNAs and diseases is taken into account.
The drawback associated with this method is it is not possible to predict the miRNAs for the
diseases which don’t have related miRNAs. Wang et al [3] showed that the relationship
between different diseases can be represented by a structure called DAG (Directed Acyclic
Graph). Disease semantic similarity was calculated first and based on that the functional
similarity between miRNAs is calculated. The Functional similarity has been utilized by
many researchers in their work for predicting the disease related miRNAs. MiRNA functional
similarity is calculated by assigning different weights based on the miRNA family and cluster
in HDMP [7], and then miRNAs are ranked by their final scores. Improved Random walk is
applied to set the scores for the candidate miRNA in MIDP [8], which implies that the
miRNA with targets has the higher possibility of being associated with disease
All the methods mentioned above have shown significant performances, but they were
unable to predict the miRNAs for the diseases which does not have any related miRNAs.
Chen et al [9] proposed a method called HGIMDA to uncover potential miRNA-disease
associations by integrating miRNA functional similarity, disease semantic similarity,
Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease
associations into a heterogeneous graph. They were able to achieve 0.87 on global LOOCV.
You et al [10] proposed the Path based MiRNA disease association by integrating known
human miRNA-disease associations, miRNA functional similarity, disease semantic
similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. In this
model heterogeneous graph of three interlinked three sub graphs is constructed and depth first
search algorithm is adopted to infer potential miRNAs associated with a disease they achieved
the reliable performance 0.916 on global LOOCV. LRSSMDA was proposed by Chen et
al[11] where they extracted miRNA functional similarity, disease semantic similarity,
Gaussian interaction profile kernel similarity and applied the Laplacian Regularized Sparse
Subspace Learning to discover potential association between miRNAs and diseases These
Rashmi J R and Lalitha Rangarajan
http://www.iaeme.com/IJARET/index.asp 143 [email protected]
methods showed outstanding performances but they faced challenges for integrating multiple
kernels in a better way.
Long et al [3] proposed WMGHMDA: a novel weighted meta-graph-based model for
predicting human microbe-disease association on heterogeneous information network.
Inspired from their method we also proposed a meta-graph model to predict miRNA disease
associations. Here we incorporated multiple sources of prior biological knowledge. This
model is capable of predicting the miRNAs for the disease without known associations. To
implement this method first heterogeneous information network is constructed by connecting
the integrated miRNA and disease similarity with the help of miRNA and disease bipartite
network.
2. MATERIALS AND METHODS
The proposed meta-graph algorithm iteratively enumerates meta-graphs associated with each
miRNA disease pair. Finally, the probability score for each miRNA disease pair is calculated
by summing up the contribution values of relevant weighted meta-graphs and prioritize
candidate miRNAs according to their probability score. In this work we have utilized miRNA
similarity based on environmental factors, miRNA similarity based on diseases, miRNA
functional similarity, disease semantic similarity, disease functional similarity. This
effectively boosts the improvement of prediction accuracy. The workflow for the proposed
method is shown below
Figure 1 Work Flow
Modified Weighted Similarity in Heterogeneous Graph for Prediction of Mirna Disease
Association
http://www.iaeme.com/IJARET/index.asp 144 [email protected]
2.1. Adjacency Matrix
We have collected 5430 miRNA disease associations from the HMDD (Human MiRNA
Disease Database). These associations are represented in the form of the adjacency matrix
A(di, mj). 383 diseases and 642 miRNAs are taken into account for study. If there is
association between a disease di and miRNA mj, A(i)(j)=1 otherwise 0.
MiRNA Functional Similarity: miRNA–miRNA functional similarity scores are
downloaded from http://cmbi.bjmu.edu.cn/misim as proposed by Wang et al [3]. It is based
on the observation that genes with similar functions are associated with common diseases.
The matrix is denoted by MFS
MiRNA Similarity based on Environmental Factors: J Ha et al [12] proposed a new
method of calculating miRNA-miRNA similarity in biological context. Environmental factors
have been recently utilized as new means of inferring the relation between miRNAs. Inspired
by this work we have calculated the miRNA Similarity based on environmental factors as
provided in MiREnvironment database [13]. The database contains 800 miRNAs and 260
environmental factors such as drugs, cigarettes, alcohol, viruses, stress, radiation etc.
MiRNA similarity based on EFs is calculated using equation 1
√ √ ⁄ (1)
MEFS(i, j) is the number of common EFs between m(i) and m(j) as in [12]. T(i, i) denote
the sum of common EFs between m[i] and all other miRNAs (is nothing but sum of ith
row
elements of MEFS).Similarly, T(j, j) is similarity computed as the jth
column sum of MEFS
2.2. MiRNA Similarity based on Diseases
MiRNA Similarity based on diseases is calculated with the help of HMDD database in a
similar manner as in the case of MEFS. The following equation is used
√ √ ⁄ (2)
All the above 3 matrices are added and average is calculated to get the average Similarity
matrix and named as FMS
Final miRNA similarity [FMS] is calculated by using equation
FMS (i, j) = ((MFS(i, j)+MMEFS(i, j)+MMDS(i, j))/3 (3)
2.3. Disease Semantic Similarity
Disease Semantic similarity DSS is calculated as proposed in Wang et al [3] work. MeSH
database (http://www.ncbi.nlm.nih.gov/) provides a system for disease classification. It is
helpful for studying the relationship between diseases. Diseases can be described using a
DAG (Directed Acyclic Graph) in which the nodes represent different diseases while the link
represents the relationship between nodes. A disease D can be represented as ,
where represents the ancestor nodes of D including D itself, represents set of
corresponding links and Taking into account disease DAG, the contribution of
disease D can be calculated using the following equation.
{
{ } (4)
∑ (5)
Rashmi J R and Lalitha Rangarajan
http://www.iaeme.com/IJARET/index.asp 145 [email protected]
With the increase in distance between disease D and its ancestor diseases their
contribution to the disease D decreases. Contribution of disease D to itself is one as it is at the
0th
layer. Contribution from ancestor disease is multiplied by semantic contribution decay
factor ∆ [0, 1]. According to literature [15, 16] the best value for ∆ is 0.5. Two diseases
sharing common parts of DAG will have higher semantic similarity. Disease semantic
similarity is calculated as per the formula in equation 6.
( ) ∑ ( )
( ) (6)
2.4. Disease Functional Similarity
Disease functional similarity scores are downloaded from https://github.com/guofei-tju/MDA-
SKF as proposed in Jiang et al [18]. We denote it by DFS
We calculate the Final Disease Similarity as in equation 7
FDS = (DSS(i, j)+DFS(i, j))/2 (7)
3. MODIFIED WEIGHTED META -GRAPH ALGORITHM FOR MIRNA
DISEASE ASSOCIATION (MWMGMDA)
As a first step, we construct the heterogeneous information network inspired from Long et
al[2] as shown in Figure 2 using FMS and FDS (equations 3, 7) and adjacency matrix of
disease and miRNA associations.
Figure 2 Heterogeneous Network
Meta-graph search algorithm is designed and implemented on this HIN. We get the
probability scores for each pair of miRNAs and diseases. These probability scores are ranked
in order to predict the disease related miRNAs.
Meta-graphs have got wide application and have been used in the representational
learning and recommendation system [20]. Inspired by WMGHMDA [2], we have used Meta-
graph concept to predict the missing associations between miRNA and disease. Meta-graph is
subset of HIN [2]. Formally, Meta-graph could be defined as sub-graph Gs = (V, E), where V
= {di/i = 1, 2, . . . , nd}∪{mj/j = 1, 2, . . . , nm} represents the set of nodes including diseases
and miRNAs, and E = {(ei, ej)} implies the set of edges including inter-layer relationship
connections in the bipartite network and intra-layer similarity connections in both disease
Modified Weighted Similarity in Heterogeneous Graph for Prediction of Mirna Disease
Association
http://www.iaeme.com/IJARET/index.asp 146 [email protected]
similarity network and miRNA similarity network[20]. In this work we take six meta-graphs
for consideration, which are shown below:
Similarity linking
Bipartite linking
Figure 3 Meta-graphs
These Meta-graphs depict possible semantic relation between a seed disease and target
miRNAs. Product of all weight values of the edges existing in the Meta-graph is calculated.
Each meta-graph contributes to the prediction probability of the miRNA disease association
pair. Contribution values of the Meta-graph when there is no observed relationship between
them is
( ) ∑ ∑
(8)
In this method we generalize common un-weighted meta-graph to weighted meta-graph
.In weighted meta-graph the weight values of the intra layer edges represent the similarities
between diseases or miRNAs and the weight values between the intra layer edges denote the
possibility of the existence of the association between disease and miRNAs. If miRNA is
experimentally verified to be associated with disease weight values of the corresponding
edges equals to 1 otherwise 0. The importance of the meta-graph decreases as the number of
the intermediate nodes and the number of edges increase, Hence, in this work we have taken
into consideration only six meta-graphs to keep the number of edges less than 5 and number
of nodes less than 3. Only the graph with single path and dual path are taken into account.
Contribution from each meta-graph is calculated according to the formulas given below
(equations 9 to14)
( ) ( ) (9)
( ) ∑ ( ) (10)
Rashmi J R and Lalitha Rangarajan
http://www.iaeme.com/IJARET/index.asp 147 [email protected]
( ) ∑ ( ) (11)
( ) ∑ ∑
(12)
( ) ∑ ∑
(13)
( )
∑ ∑ ( )
(14)
Here all the meta-graphs have different structures and characteristics so their contributions
to the predictions of the miRNAs disease associations will be biased so we introduce the bias
rating to describe the differential contribution of the different meta-graphs. Differences
between meta-graphs mainly depend upon on the number of given nodes which is directly
connected to the bipartite edge could be seed disease or target miRNA Each of the graphs
have 2,1,1,0,1,1 nodes respectively as there is no edge between seed disease or target miRNA
graph(d) has 0 nodes. Based on the assumption meta-graph with more nodes have more
potential. Graph ‘a’ has more potential to contribute to the disease miRNAs association, ‘d’
is known to contribute least and ‘e’ &‘f ’can contribute considerably in disease association
pair. e & f are dual path weighted graph as in this graph seed node has more semantic paths
connected to a target miRNA node. We have assigned different bias ratings for different
weighted meta-graphs based on their contribution.
Here we apply weighted meta-graph algorithm of HIN to traverse all relevant meta-
graphs. The prediction score P is defined and calculated by summing up the contribution
values of the meta-graphs as follows
( ) ∑ ∑
(15)
is the contribution value of the rth meta-graph which belongs to l
th type of weighted
meta-graph to the pair ( . Here N=6, is a category number of weighted meta-graph as 6
types of meta-graphs are taken into the consideration for study. M is the number of meta-
graphs that are included in each category, λ [0, 1] is the bias rating applied to different
meta-graphs in order to distinguish their contributions to final predictions probability. We
iteratively implement the above search process which is based weighted meta-graph search
algorithm until Pt converges. Iterative formula with matrix is shown below
(16)
In the above equation Id and Im denote the unit matrices of size nd (no of diseases) and
nm(no of miRNAs). λ values are empirically set as , , , , , 0.2. is the decay factor which is similar to restart probability
in the random walk with restart. The initial values of the matrix Pt is set to the normalized
values of a (adjacent Matrix) A(di, mj). The element in the matrix Pt at ith
row and jth
column is
the probability value of the disease association pair. is the Hambard Product. To control
the contributions of the dural paths we set the value of the γ=0.1. According to Wang et al
[26] the probabilities computed in equation 11 will converge if the Average miRNA similarity
(FMS) and Average disease similarity (FDS) are properly normalized according to equations
17 and 18
( ) ( )
√∑ √∑ ( )
(17)
( ) ( )
√∑ √∑ ( )
(18)
Modified Weighted Similarity in Heterogeneous Graph for Prediction of Mirna Disease
Association
http://www.iaeme.com/IJARET/index.asp 148 [email protected]
4. PERFORMANCE EVALUATION
We have used global LOOCV based on the known miRNA-disease associations in HMDD
database to evaluate the performance of WMGMDA. Further, WMGMDA are compared with
two previous classical computational methods: HGIMDA [22], RLSMDA [23]. In LOOCV
evaluation, each known association in the database is regarded as the test sample in turn,
while the other known associations are regarded as training samples. The miRNA-diseases
without known association evidences are considered as candidate samples. The scores of all
miRNA-disease pairs could be obtained after WMGMDA was implemented. In global
LOOCV, the score of the test sample was compared with the scores of all the candidate
samples.
Finally, a Receiver Operating Characteristics curve (ROC) compares WMGMDA with all
the previous methods. In this curve, the true positive rate (TPR, sensitivity) and false positive
rate (FPR, 1-specificity) are plotted [24]. Sensitivity represents the percentage of miRNA-
disease test samples whose ranks exceeded the given threshold while specificity represents the
percentage of negative miRNA-disease associations whose ranks were lower than the
threshold [25]. The area under the ROC (AUC) was calculated to evaluate the accuracy of
MWMGMDA. If AUC=1, MWMGMDA proves to be a prefect performance. Having AUC
0.5 means that the method merely has a random prediction performance. The AUCs of
MWMGMDA, NPCMDA [27], RLSMDA [28] are 0.9617833, 0.9148398, and 0.8549943
respectively in global LOOCV.
Figure 4 ROC curve
--------- our method
---x---x---- RLSMDA
o-o-o-o NPCMDA
It can be observed that, the proposed method performs better than RLSMDA and
NPCMDA.
Another performance evaluation is through leave one disease out strategy. We deleted all
the miRNA disease associations associated with a particular disease. The proposed method is
able to predict the miRNAs associated with the given diseases. This demonstrates that
proposed method is capable of predicting diseases which doesn’t have any associated
miRNAs.
Rashmi J R and Lalitha Rangarajan
http://www.iaeme.com/IJARET/index.asp 149 [email protected]
We studied the effect of the parameter μ on the performance of proposed method. μ can
take values between 0 to 0.9. We have calculated the AUC of the proposed method at
different values of μ we were able to get the best performance at 0.2. The table below shows
the AUC obtained at different values of μ.
Table 1 AUC Performance
μ. AUC
0.1 0.953447
0.2 0.961972
0.3 0.951489
0.4 0.93862
0.5 0.9175464
0.6 0.895443
0.7 0.873503
0.8 0.85854
0.9 0.83
5. RESULTS
We have predicted the miRNAs for the 10 diseases which are given in Table 2.
Table 2 Results
Disease Number MiRNAs Confirmed in Top 50 Predictions
Kidney Cancer 50
Ovarian Cancer 48
Thyroid Cancer 45
Lung Cancer 44
Pancreatic Cancer 49
Brain Cancer 43
Leukemia 48
Cervical Cancer 47
Stomach Cancer 49
Breast Cancer 45
Top 50 predictions for 3 diseases are shown in Tables 3 to 5
MiRNAs predicted by the proposed method are confirmed by the three experimentally
verified databases namely dbDEMC[5], MirCANCER [28], HMDD 3.0[6]
Kidney Cancer
Table 3 Top 50 predictions for Kidney Cancer
MiRNA Confirmed by
hsa-mir-155 dbDEMC
hsa-mir-146a dbDEMC, HMDD 3.0,MiRCancer
hsa-mir-125b dbDEMC, MiRCancer
hsa-mir-210 dbDEMC, HMDD 3.0
hsa-mir-145 dbDEMC, HMDD 3.0
hsa-mir-126 dbDEMC, HMDD 3.0
hsa-mir-34a dbDEMC
hsa-mir-17 dbDEMC, HMDD 3.0
hsa-mir-29a dbDEMC
hsa-mir-27a dbDEMC
Modified Weighted Similarity in Heterogeneous Graph for Prediction of Mirna Disease
Association
http://www.iaeme.com/IJARET/index.asp 150 [email protected]
hsa-mir-221 dbDEMC, MiRCancer
hsa-mir-200b dbDEMC
hsa-mir-223 dbDEMC
hsa-mir-20a dbDEMC
hsa-mir-140 dbDEMC
hsa-mir-31 dbDEMC
hsa-mir-18a dbDEMC
hsa-mir-146b dbDEMC
hsa-mir-19a dbDEMC
hsa-mir-218 dbDEMC
hsa-mir-34c dbDEMC
hsa-mir-125a dbDEMC
hsa-let-7c dbDEMC
hsa-mir-106b dbDEMC, HMDD 3.0
hsa-mir-195 dbDEMC
hsa-mir-200a dbDEMC
hsa-mir-222 dbDEMC
hsa-let-7b dbDEMC
hsa-mir-423 dbDEMC
hsa-mir-107 dbDEMC
hsa-mir-30a dbDEMC, HMDD3.0, MiRCancer
hsa-mir-302b dbDEMC
hsa-mir-199a dbDEMC
hsa-mir-143 dbDEMC, MiRCancer
hsa-mir-139 dbDEMC
hsa-mir-205 dbDEMC
hsa-mir-122 dbDEMC
hsa-mir-196a dbDEMC
hsa-mir-34b dbDEMC
hsa-mir-150 dbDEMC
hsa-let-7g dbDEMC
hsa-mir-16 dbDEMC
hsa-mir-373 dbDEMC
hsa-mir-135b dbDEMC
hsa-let-7e dbDEMC
hsa-mir-20b dbDEMC
hsa-mir-99b dbDEMC
hsa-mir-130a dbDEMC
hsa-mir-99a dbDEMC
hsa-mir-429 dbDEMC
Ovarian Cancer
Table 4 Top 50 predictions for Ovarian Cancer
MiRNA Confirmed by
hsa-mir-210 dbDEMC, HMDD 3.0
hsa-mir-98 dbDEMC,HMDD 3.0
hsa-mir-139 dbDEMC,HMDD 3.0
hsa-mir-342 dbDEMC
hsa-mir-195 dbDEMC,HMDD 3.0
hsa-mir-15a dbDEMC
Rashmi J R and Lalitha Rangarajan
http://www.iaeme.com/IJARET/index.asp 151 [email protected]
hsa-mir-222 dbDEMC,HMDD 3.0,MirCancer
hsa-mir-143 dbDEMC,HMDD 3.0
hsa-mir-150 dbDEMC,HMDD 3.0
hsa-mir-196a dbDEMC,MirCancer
hsa-mir-10a dbDEMC
hsa-mir-373 dbDEMC,HMDD 3.0
hsa-mir-122 dbDEMC
hsa-mir-423 dbDEMC
hsa-mir-206 HMDD 3.0
hsa-mir-23a dbDEMC,HMDD 3.0
hsa-mir-520d dbDEMC
hsa-mir-7 dbDEMC,HMDD 3.0
hsa-mir-106a dbDEMC,HMDD 3.0
hsa-mir-15b dbDEMC,HMDD 3.0
hsa-mir-142 dbDEMC,HMDD 3.0
hsa-mir-29c dbDEMC,HMDD 3.0
hsa-mir-212 dbDEMC,HMDD 3.0
hsa-mir-484 dbDEMC
hsa-mir-204 dbDEMC
hsa-mir-27b dbDEMC,HMDD 3.0
hsa-mir-181b dbDEMC,HMDD 3.0
hsa-mir-608 UNCONFIRMED
hsa-mir-26a dbDEMC,HMDD 3.0
hsa-mir-199b dbDEMC,HMDD 3.0
hsa-mir-203 HMDD 3.0
hsa-mir-193a dbDEMC,HMDD 3.0
hsa-mir-483 dbDEMC
hsa-mir-132 dbDEMC,HMDD 3.0
hsa-mir-107 dbDEMC,HMDD 3.0
hsa-mir-23b dbDEMC,HMDD 3.0
hsa-mir-181a dbDEMC,HMDD 3.0
hsa-mir-345 dbDEMC
hsa-mir-137 dbDEMC,HMDD 3.0
hsa-mir-330 dbDEMC,HMDD 3.0
hsa-mir-301a dbDEMC
hsa-mir-144 dbDEMC
hsa-mir-196b HMDD 3.0
hsa-mir-181c dbDEMC
hsa-mir-134 dbDEMC,HMDD 3.0
hsa-mir-424 dbDEMC
hsa-mir-326 dbDEMC
hsa-mir-454 dbDEMC,HMDD 3.0
hsa-mir-218-2 UNCONFIRMED
hsa-mir-205 dbDEMC,HMDD 3.0
Modified Weighted Similarity in Heterogeneous Graph for Prediction of Mirna Disease
Association
http://www.iaeme.com/IJARET/index.asp 152 [email protected]
Pancreatic Cancer
Table 5 Top 50 predictions for Pancreatic Cancer
MiRNA Confirmed By
hsa-mir-29a dbDEMC,HMDD3.0,MirCANCER
hsa-mir-99b dbDEMC
hsa-mir-342 dbDEMC
hsa-mir-181d dbDEMC
hsa-mir-335 dbDEMC,HMDD3.0,MirCANCER
hsa-mir-125a dbDEMC
hsa-mir-19a dbDEMC
hsa-mir-20b dbDEMC
hsa-mir-106b dbDEMC
hsa-mir-195 dbDEMC,HMDD3.0,MirCANCER
hsa-mir-30d dbDEMC
hsa-mir-302b dbDEMC
hsa-mir-30a dbDEMC
hsa-mir-22 dbDEMC
hsa-mir-23b dbDEMC
hsa-mir-141 dbDEMC,HMDD3.0,MirCANCER
hsa-mir-373 dbDEMC,HMDD3.0,MirCANCER
hsa-mir-424 dbDEMC,HMDD3.0,MirCANCER
hsa-mir-520d dbDEMC
hsa-mir-7 dbDEMC,HMDD3.0,MirCANCER
hsa-mir-148b dbDEMC,HMDD3.0,MirCANCER
hsa-mir-370 dbDEMC
hsa-mir-206 dbDEMC
hsa-mir-144 dbDEMC
hsa-mir-29c dbDEMC,HMDD3.0,MirCANCER
hsa-mir-27b dbDEMC
hsa-mir-19b dbDEMC
hsa-mir-137 dbDEMC,HMDD3.0,MirCANCER
hsa-mir-181a dbDEMC,HMDD3.0,MirCANCER
hsa-mir-520c dbDEMC
hsa-mir-320a dbDEMC,HMDD3.0,MirCANCER
hsa-mir-30b dbDEMC
hsa-mir-181c dbDEMC,HMDD3.0,MirCANCER
hsa-mir-497 dbDEMC,HMDD3.0,MirCANCER
hsa-mir-130b dbDEMC
hsa-mir-330 dbDEMC
hsa-mir-449b dbDEMC
hsa-mir-134 dbDEMC
hsa-mir-193b dbDEMC
hsa-mir-26b dbDEMC
hsa-mir-372 UNCONFIRMED
hsa-mir-423 dbDEMC
hsa-mir-326 dbDEMC
hsa-mir-149 dbDEMC
hsa-mir-503 dbDEMC
hsa-mir-339 dbDEMC
hsa-mir-874 dbDEMC
hsa-mir-490 dbDEMC
hsa-mir-130a dbDEMC
hsa-mir-9 dbDEMC,HMDD3.0,MirCANCER
Rashmi J R and Lalitha Rangarajan
http://www.iaeme.com/IJARET/index.asp 153 [email protected]
6. CONCLUSION
Weighted meta-graph for disease association is very effective in predicting miRNAs
associated with disease. We are able to predict the miRNAs for the diseases which are not
having any associations. In the proposed method we have integrated miRNA functional data,
disease based miRNA functional data, and Environmental factors data, Disease functional
Data, Disease semantic data. Rigorous experiments, performance comparison with other
methods and cross validation suggest the proposed method performs better. In future to
improve the prediction accuracy we are contemplating to integrate MiRNA target
information.
REFERENCES
[1] Chen X, Liu MX, Yan GY. RWRMDA: predicting novel human microRNA-disease
associations. Mol Biosyst. 2012; 8:2792–98. https://doi.org/10.1039/c2mb25180a
[2] Long, Y., & Luo, J. (2019). Open Access WMGHMDA : a novel weighted meta-graph-based
model for predicting human microbe-disease association on heterogeneous information
network, 1–18.
[3] Wang, D., Wang, J., Lu, M., Song, F., & Cui, Q. (2010). Inferring the human microRNA
functional similarity and functional network based on microRNA-associated diseases
Availability :, 26(13), 1644–1650. https://doi.org/10.1093/bioinformatics/btq241
[4] Xuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., et alc(2013). Correction: prediction of
micrornas associated with human diseases based on weighted k most similar neighbors. PLoS
ONE 8:e70204. doi: 10.1371/annotation/a076115e-dd8c-4da7-989d-c1174a8cd31e
[5] Yang, Z., Ren, F., Liu, C., He, S., Sun, G., Gao, Q., et al. (2010). DBDEMC: a database of
differentially expressed miRNAs in human cancers. BMC Genomics 11(Suppl. 4):S5. doi:
10.1186%2F1471-2164-11-S4-S5
[6] Li, Y., Qiu, C., Tu, J., Geng, B., Yang, J., Jiang, T., et al. (2014). HMDD v2.0: a database for
experimentally supported human microRNA and disease associations. Nucleic Acids Res.
42(Database issue), D1070–D1074. doi: 10.1093/nar/gkt1023
[7] Xuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., et al. (2013). Correction: prediction of
micrornas associated with human diseases based on weighted k most similar neighbors. PLoS
ONE 8:e70204. doi: 10.1371/annotation/a076115e-dd8c-4da7-989d-c1174a8cd31e
[8] Xuan, P., Han, K., Guo, Y., Li, J., Li, X., Zhong, Y., et al. (2015). Prediction of potential
disease-associated microRNAs based on random walk. Bioinformatics 31:1805. doi:
10.1093/bioinformatics/btv039
[9] HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction Xing
Chen1, Cheng gang Clarence Yan, Xu Zhang, Zhu-Hong You, Yu-An Huang, Gui-Ying Yan
[10] You, Z. H., Huang, Z. A., Zhu, Z., Yan, G. Y., Li, Z. W., Wen, Z., et al. (2017). PBMDA: a
novel and effective path-based computational model for miRNA-disease association
prediction. PLoS Comput. Biol. 13:e1005455. doi: 10.1371/journal.pcbi.1005455
[11] Winter J., Diederichs S. (2011) MicroRNA Biogenesis and Cancer. In: Wu W. (eds)
MicroRNA and Cancer. Methods in Molecular Biology (Methods and Protocols), vol 676.
Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-863-8_1
[12] Jihwan Haa, Hyunjin Kima, Youngmi Yoonb and Sanghyun Parka, * A method of extracting
disease-related microRNAs through the propagation algorithm using the environmental factor
Modified Weighted Similarity in Heterogeneous Graph for Prediction of Mirna Disease
Association
http://www.iaeme.com/IJARET/index.asp 154 [email protected]
based global miRNA network Bio-Medical Materials and Engineering 26 (2015) S1763–
S1772, DOI 10.3233/BME-151477, IOS Press
[13] Q. Yang, C. Qiu, J. Yang, Q. Wu and Q. Cui, MIRenvironment database: Providing a bridge
for microRNAs, environmental factors and phenotypes, Bioinformatics 27 (2011), 3329–
3330)
[14] Ambros V (2004) The functions of animal microRNAs. Nature 431: 350±355.
https://doi.org/10.1038/nature02871 PMID: 15372042
[15] Chen X, You Z, Yan G, Gong D (2016) IRWRLDA: Improved Random Walk with Restart for
LncRNADisease Association prediction. Oncotarget 7: 57919±57931.
https://doi.org/10.18632/oncotarget.11141 PMID: 27517318
[16] Huang Y, Chen X, You Z, Huang D, Chan K (2016) ILNCSIM: improved lncRNA functional
similarity calculation model. Oncotarget 7: 25902±25914.
https://doi.org/10.18632/oncotarget.8296 PMID: 27028993
[17] van Laarhoven T, Nabuurs SB, Marchiori E (2011) Gaussian interaction profile kernels for
predicting drug-target interaction. Bioinformatics 27: 3036±3043.
https://doi.org/10.1093/bioinformatics/btr500 PMID: 21893517
[18] Dong Y, Chawla NV, Swami A. metapath2vec: Scalable representation learning for
heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference
on Knowledge Discovery and Data
[19] Mining; 2017. p. 135–44.
[20] Zhao H, Yao QM, Li JD, Song YQ, Lee DL. Meta-Graph based recommendation fusion over
heterogeneous information networks. In Proceedings of the 23rd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining; 2017. p. 635–44. https://doi.org/
10.1145/3097983.3098063.
[21] Fu TY, Lee WC, Lei Z. Hin2vec: Explore meta-paths in heterogeneous information networks
for representation learning. In: International Conference on Information and Knowledge
Management; 2017.p. 1797–806. https://doi.org/10.1145/3132847.3132953.
[22] Shi Y, Gui H, Zhu Q, Kaplan L, Han JW. Aspem: Embedding learning by aspects in
heterogeneous information networks. In: International Conference on Data Ming; 2018. arXiv
preprint arXiv:1803.01848
[23] Chen X, Yan C, Zhang X, You Z, Huang Y, Yan G. HGIMDA: Heterogeneous Graph
Inference for MiRNA-Disease Association prediction. Oncotarget. 2016; 7:65257- 65269. doi:
10.18632/oncotarget.11251.
[24] Chen X, Yan GY. Semi-supervised learning for potential human microRNA-disease
associations inference. Sci Rep. 2014; 4:5501.
[25] Jiang Q, Hao Y, Wang G, Juan L, Zhang T, Teng M, Liu Y, Wang Y. Prioritization of disease
microRNAs through a human phenome-microRNAome network. BMC Syst Biol. 2010; 4:S2.
[26] Oved K, Morag A, Pasmanikchor M, Oronkarni V, Shomron N, Rehavi M, Stingl JC, Gurwitz
D. Genome-wide miRNA expression profiling of human lymphoblastoid cell lines identifies
tentative SSRI antidepressant response biomarkers. Pharmacogenomics. 2012; 13:1129-1139.
Rashmi J R and Lalitha Rangarajan
http://www.iaeme.com/IJARET/index.asp 155 [email protected]
[27] Wang WH, Yang S, Li J. Drug target predictions based on heterogeneous graph inference. In:
Proceedings of the Pacific Symposium; 2013. p. 53–64.
https://doi.org/10.1142/9789814447973_0006.
[28] Chen, X., Yan, G. Semi-supervised learning for potential human microRNA-disease
associations inference. Sci Rep 4, 5501 (2015). https://doi.org/10.1038/srep05501Chen, X.,
Yan, G. Semi-supervised learning for potential human microRNA-disease associations
inference. Sci Rep 4, 5501 (2015). https://doi.org/10.1038/srep05501
[29] Gu, C., Liao, B., Li, X. et al. Network Consistency Projection for Human miRNA-Disease
Associations Inference. Sci Rep 6, 36054 (2016). https://doi.org/10.1038/srep36054
[30] Boya Xie, Qin Ding, Hongjin Han, Di Wu, miRCancer: a microRNA–cancer association
database constructed by text mining on literature, Bioinformatics, Volume 29, Issue 5, 1
March 2013, Pages 638–644, https://doi.org/10.1093/bioinformatics/btt014
[31] Ambros V (2001) microRNAs: tiny regulators with great potential. Cell 107: 823±826. PMID:
11779458