# MuCoMiD: A Multitask graph Convolutional Learning Framework for miRNA-
Disease Association Prediction

Ngan Dong1, Stefanie Mücke2, Megha Khosla1
1 L3S Research Center, Leibniz University of Hannover, Hannover, Germany
2 TRAIN Omics, Translational Alliance in Lower Saxony, Hannover, Germany
Email<EMAIL_ADDRESS>
###### Abstract

Growing evidence from recent studies implies that microRNA or miRNA could
serve as biomarkers in various complex human diseases. Since wet-lab
experiments are expensive and time-consuming, computational techniques for
miRNA-disease association prediction have attracted a lot of attention in
recent years. Data scarcity is one of the major challenges in building
reliable machine learning models. Data scarcity combined with the use of
precalculated hand-crafted input features has led to problems of overfitting
and data leakage.

We overcome the limitations of existing works by proposing a novel multi-
tasking graph convolution-based approach, which we refer to as MuCoMiD.
MuCoMiD allows automatic feature extraction while incorporating knowledge from
five heterogeneous biological information sources (interactions between
miRNA/diseases and protein-coding genes (PCG), interactions between protein
coding genes, miRNA family information, and disease ontology) in a multi-task
setting which is a novel perspective and has not been studied before. To
effectively test the generalization capability of our model, we construct
large-scale experiments on standard benchmark datasets as well as our proposed
larger independent test sets and case studies. MuCoMiD shows an improvement of
at least $\sim 3$% in 5-fold CV evaluation on HMDDv2.0 and HMDDv3.0 datasets
and at least $\sim 35$% on larger independent test sets with unseen miRNA and
diseases over state-of-the-art approaches. We share our code for
reproducibility and future research at https://git.l3s.uni-
hannover.de/dong/cmtt.

###### Index Terms:

graph representation learning, multitask, data integration, miRNA, disease

##  1 Introduction

Beginning in the early 2000s, the biological dogma that proteins are
responsible for most functions in a cell began to shift and new classes of
non-coding, regulatory RNAs became points of interest. The highly conserved
class of microRNAs (miRNAs) with an approximate length of 22 nucleotides were
first considered “junk” DNA without function and emerged as regulators in cell
development, maturation, differentiation, and apoptosis of the cell, cell
signaling, cellular interactions, and homeostasis [1, 2, 3].

MicroRNAs fulfill their diverse functions by regulating gene expression of
protein-coding genes (PCGs) after transcription. The transcribed mRNA of PCGs
can be directly bound by miRNAs, which leads to cleavage or destabilization of
the mRNA and represses the translation into proteins [4]. Each miRNA can have
hundreds of target mRNAs and mRNAs can be regulated by more than one miRNA,
resulting in complex networks that are yet to be fully understood. The
mutation of miRNAs or changes in their expression can have widespread
consequences that can be hard to predict.

Consequently several associations between miRNAs and diseases have been
confirmed using biological experiments leading to the belief that miRNAs could
be potential biomarkers in certain diseases such as cancers or immune-related
diseases [5, 6, 7, 8, 9, 10, 11]. Besides unveiling a deeper understanding of
diseases’ molecular pathogenesis, identifying potential associations between
miRNAs and diseases can also help in the treatment and discovery of possible
drug targets.

Owing to the significance of the problem and the time-consuming nature of
biological experiments, recent years have seen an upsurge in machine learning
approaches [12, 13, 14, 15, 16] for _predicting miRNA-disease associations_.
Being successfully able to predict miRNA-disease associations can lead to
information prioritisation in biological wet-lab experimentation, which will
result in considerable time and cost savings. From a machine learning
perspective, the problem of predicting miRNA-disease associations can be
formulated as that of a _link prediction problem_ in a bipartite graph, where
miRNA and disease form the two sets of nodes. However, as is typical to many
biomedical applications, a major challenge for building generalizable and
eventually well-performing models for the miRNA-disease association prediction
problem is _data scarcity_ of miRNA-disease data. For example, the total
number of miRNA and disease nodes (without any preprocessing and removal of
duplicates) in the standard HMDD v2.0 [17] database are 495 and 383
respectively with a total of 5,430 associations (links). These networks are
therefore not only small with respect to the nodes set but are also sparsely
connected.

Data scarcity often leads to biased and non-generalizable models. Earlier
attempts to address data scarcity rely on creating additional secondary
features based on the initial feature set by computing intra-node
similarities, e.g., miRNA functional similarity. So much so, that these
secondary features that encode miRNA functional similarity are pre-computed
and are even deposited in well-known databases like MISIM [18]. The first
problem associated with using such similarity-based input features is that it
is prone to overfitting because slight errors in the input features get
amplified by the secondary features. Secondly, models based on secondary
features cannot be used to predict new associations for a miRNA (or a
disease), i.e., instances for which no prior disease (or miRNA) association
information is available.

More worrying is the problem of _data leakage_ when using pre-computed miRNA
functional similarities indiscriminately from available databases. Dong and
Khosla [19] find that most of the existing works that employ pre-computed
similarities in model building ignore the actual train/test split giving rise
to _data leakage_. In other words, some of the associations which are to be
tested by the model are already present in the association network that was
used to compute the similarity features. Finally, the small dataset size for
the miRNA-disease prediction task prohibits the utility of flexible and more
expressive modern representation learning approaches.

###  1.1 Present work

We overcome the above limitations in the earlier literature by avoiding the
creation of secondary features altogether. Instead, our approach attempts to
address the data scarcity problem by integrating knowledge from multiple
heterogeneous sources of information available for miRNA and diseases in
addition to the miRNA-disease associations. Combining multiple data sources
enables us to compensate for missing or unreliable information in any single
data type leading to more reliable predictions. Our key contribution is to
model the integration of heterogeneous knowledge sources into a common
representation space that can be trained end-to-end using modern
representation learning machinery.

To this end, we propose a Multitask graph Convolutational neural network for
miRNA-Disease association prediction, which we refer to as MuCoMiD for
brevity. MuCoMiD allows automatic feature extraction while incorporating
knowledge from five heterogeneous biological information sources (interactions
between miRNA/diseases and protein-coding genes (PCG), interactions between
protein-coding genes, miRNA family information, and disease ontology) in a
_multi-task setting_. Instead of pre-calculated secondary features for miRNA
and disease as in previous works, we employ graph convolution operation
(without the non-linear activation) over the corresponding biological networks
to learn informative representations for miRNA and disease automatically at
training time. Overall we have an expressive linear model with non-linear
activation only at the output layer.

The added side tasks serve as regularizers and help us to incorporate domain-
knowledge. For example, an miRNA $m$ regulates a set of proteins $p$ that are
responsible for some biological functions. Moreover, disruptions in the
biological functions of $p$ lead to certain disease condition $d$. Then $m$
has some influence over disease $d$ via $p$. The additional tasks of
predicting miRNA-PCG interaction and disease-PCG interaction help us to encode
such influence by embedding $m$ and $d$ closer in the representational space.
Besides, we employ _adaptive loss balancing_ techniques to fine-tune multi-
task loss gradients. This allows us to utilize the full power of multi-task
learning without resorting to exhaustive hyperparameter search.

We conduct an extensive evaluation of our approaches in comparison to earlier
works on existing benchmark datasets retrieved from HMDD v2.0 [17] and HMDD
v3.0 [20] databases. In addition to standard benchmark datasets, we also
construct and test on new and larger independent test sets. We finally present
case studies for three specific diseases to showcase the utility of our
approach in predicting the association of novel diseases for which no prior
miRNA association information is available in the train set.

Our Contributions. To summarize, we make the following contributions:

  * •

We model the miRNA-disease association prediction problem in a _multi-task
setting_ incorporating heterogeneous domain knowledge, which is a novel
perspective and has not been studied before for the current problem.

  * •

We construct four new larger test sets for testing in transductive (when miRNA
and disease nodes in the test set are also present in the train set) and
inductive settings (when the test set contains a number of new miRNA or
disease nodes).

  * •

We conduct large-scale experiments, ablation study and case studies for three
specific diseases to showcase the superiority of our approach.

  * •

We release all the code and data used in this work for reproducibility and
future research at https://git.l3s.uni-hannover.de/dong/cmtt.

##  2 Problem statement and related work

The miRNA-disease association data can be represented using a bipartite graph
$\mathcal{G}_{md}=(M,D,E)$ where $M$ is the set of nodes representing miRNAs
and $D$ denotes the set of disease nodes. Each edge $e=(m,d)\in E$ denotes the
association between the miRNA node $m$ and disease node $d$. We are then
interested in the following problem statement.

Problem statement Given the bipartite graph $\mathcal{G}_{md}=(M,D,E)$, we are
interested in (i) predicting missing links among the given nodes (transductive
setting) (ii) predicting new links for so far unseen novel miRNA or disease
nodes (inductive setting).

###  2.1 Related work

Existing computational approaches for miRNA-disease association prediction can
be broadly grouped into three classes: _scoring-based_ , _network topology_
and _machine learning_ based methods.

Assuming that the miRNA pairs linked to common diseases are functionally more
related scoring based methods [18, 21] proposed scoring systems to prioritize
miRNA-disease associations. A more sophisticated scoring scheme while
integrating information from miRNA and disease similarity networks was
proposed in [22]. Network based approaches [23, 24, 25] construct miRNAs
and/or disease similarity networks and aims at efficiently transferring known
miRNA-disease association labels between similar miRNAs and/or similar
diseases in the network. Chen et al. [23] employ repeated random walks with
restarts over the miRNA functional similarity network and prioritize candidate
miRNA-disease associations using the final stable walk probability.

More closely related to our work is the third category of machine learning
based methods. Approaches in this category mainly rely on using secondary or
hand-crafted features to construct similarity networks from which latent node
features are extracted using graph-based representation learning techniques.
Epmda [12] extracts edge perturbation-based features from the miRNA-disease
heterogeneous network and then trains a Multilayer Perceptron regression model
to prioritize miRNA-disease associations. NNMDA [26] combines information from
five different miRNA similarities and two disease similarities to build a
heterogeneous network for feature learning and association prediction. [27]
incorporates information from multiple domains, for example, miRNA-lncRNA and
miRNA-PCG interaction, miRNA-drug association, disease-lncRNA, disease-PCG
association, disease-drug association, to build a heterogeneous information
network for feature extraction. The graph-based features along with miRNA
k-mer feature (calculated from the miRNA sequence) and disease semantic
similarity are concatenated to form the input to a Random Forest classifier
for association prediction.

Another line of works include Nimgcn [16] and DimiG [15] which propose end-to-
end learning approaches in which graph convolution networks (GCNs)[28] are
employed for extracting latent features of miRNA and disease nodes. Non-graph
based approaches like Dbmda [13] extract latent features from input hand-
crafted features consisting of miRNA functional, disease semantic, and miRNA
sequence similarity using autoencoders. The latent features are then fed to a
Rotation Forest [29] classifier.

The reliance on hand-crafted features based on existing association data
limits the applicability of existing techniques to transductive settings.
Hence, apart from other limitations with respect to model generalization and
data leakage (already discussed in Section 1) a majority of these approaches
cannot be applied to predict associations for new miRNA or disease nodes that
have not been observed in training data.

##  3 Proposed approach

Figure 1: A schematic diagram of MuCoMiD. MuCoMiD consists of three main
modules: (i) input graph construction in which we build networks corresponding
to the available side information from miRNA family, PCG-PCG interactions, and
disease ontology (ii) the second module takes the constructed networks as
input and generates the nodes’ representation according to their local
neighbors (iii) finally, one classifier for miRNA-disease association
prediction, two regressors for miRNA-PCG and disease-PCG interaction
confidence score prediction are added. The second and third modules get
trained jointly using a multitask loss. The multitask loss is a weighted sum
of the three individual task losses and is optimized using a _dynamic loss
balancing_ technique.

Given the input bipartite miRNA-disease network $\mathcal{G}_{md}$, we treat
the miRNA-disease association problem as a binary classification where the
label for an input pair node $(m,d)$ is $1$ if there is a known association
between them and $0$ otherwise.

To overcome the challenges of data scarcity, we propose MuCoMiD in which we
focus on _effectively integrating heterogeneous biological information while
learning to predict missing or new miRNA-disease associations_. Instead of
relying on secondary or handcrafted features, we rely on other knowledge
sources to learn the input representation automatically at training time. Also
recent works indicate that PCGs are the most important link between miRNAs and
their associated diseases [30] since changes in miRNAs lead to differently
regulated PCGs, which in turn can cause diseases. Therefore, in addition to
the miRNA-disease association prediction task (formulated as a binary
classification problem), we add miRNA-PCG and disease-PCG interaction
confidence score prediction (formulated as regression tasks) as two additional
side tasks to incorporate additional domain knowledge and prevent overfitting.

In summary, MuCoMiD employs different ways of integrating domain knowledge at
different stages of the model building process. In particular, information
from three different biological networks namely, miRNA family , PCG-PCG
interaction, and disease ontology is directly used as inputs to learn the node
representations during training. Besides, the miRNA-PCG and disease-PCG
interactions are employed to build additional regularization objective
functions. The incorporation of various information sources help compensate
for the lack of information in single data source. It also helps in mitigating
the data scarcity problem.

In the following we describe the three modules of MuCoMiD (also depicted in
Figure 1): (i) _input graph construction_ , (ii) _graph convolution based
feature extraction_ , and (iii) _multi-task optimization/learning_.

###  3.1 Input graph construction

We start by describing the construction or retrieval of various biological
networks that we leverage as additional sources of information and the
corresponding rationale.

miRNA family, $\mathcal{G}_{m}$. A miRNA family is the group of miRNAs that
share a common ancestor in the phylogenetic tree. miRNAs that belong to the
same family usually have highly similar sequences, secondary structures and
tend to execute similar biological functions [31]. Similar miRNA would tend to
participate in the mechanisms of similar diseases. We retrieve miRNA family
information from mirBase database [32]. The miRNA network $\mathcal{G}_{m}$ is
an unweighted undirected graph in which there is a connection between node A
and node B if A and B belong to the same family. Figure 2 presents an
illustration of the miRNA family network generated from our data.

Figure 2: An illustration of the miRNA family network.

Disease ontology, $\mathcal{G}_{d}$. The disease ontology [33] represents the
disease etiology classes. A directed connection between two diseases exists if
there exists a is-a relationship between them. Similar diseases can be
expected to interact with similar miRNAs. The disease ontology network
$\mathcal{G}_{d}$ is an unweighted directed network in which there is a
directed connection from A to B if B is a parent of A. $\mathcal{G}_{d}$ can
be visualized as a directed tree which contains only directed connection
between children and parents nodes. Each tree layer represents one layer of
abstraction. The upper most layer represents the most general disease
category. An illustration of the disease ontology is given in figure 3.

Figure 3: An illustration of the disease ontology. Directed edges denote
‘is_a’ relations

PCG-PCG interaction, $\mathcal{G}_{p}$. PCGs interact with PCGs to carry out
biological functions. Therefore, given the fact that protein coding gene
$p_{1}$ activates the expression of protein coding gene $p_{2}$, if the miRNA
$m$ can regulate $p_{1}$ then there should be some relation between $m$ and
$p_{2}$. In other words, information from the protein-protein interaction
network will bring additional insights about indirect relationship between
miRNAs/diseases and the rest of PCGs with which a direct interaction in not
known. An example of the miRNA-PCG, PCG-PCG interactions is presented in
figure 4 where red nodes and red edges denote miRNAs and miRNA-PCG connections
while blue nodes and blue edges represent PCGs and PCG-PCG interactions,
respectively. We download PCG interaction data from STRING v10 database [34].
As preprocessing step, we retain only interactions between PCGs that have at
least one known interaction with miRNAs or diseases. The PCG network
$\mathcal{G}_{p}$ is an undirected weighted network in which the edge weights
correspond to the confidence score of PCG-PCG interaction retrieved from
STRING database.

Figure 4: An illustration of miRNA-PCG, PCG-PCG interactions. Red nodes and
red edges denote miRNAs and miRNA-PCG connections while blue nodes and blue
edges represent PCGs and PCG-PCG interactions, respectively.

###  3.2 Graph convolution-based feature extraction

Having constructed the relevant networks we next extract informative node
representations using the node neighborhood information. As we have no input
node features, we train embedding layers (of dimension 32) for each of the
network.

An embedding layer is essentially a look-up table where $i$th row corresponds
to the learnt representation of $i$th node. The node embedding is then passed
as an input feature to the graph convolutional layer. A graph convolutional
layer is essentially a linear layer which transforms the node feature as an
aggregation of representations of its 1-hop neighbors. In particular for input
adjacency matrix $\mathbf{A}$ and node embedding matrix $\mathbf{X}$ we obtain
transformed node feature matrix $\mathbf{X}^{\prime}$ as follows

$\displaystyle\mathbf{X}{{}^{\prime}}=\mathbf{\hat{D}}^{-1/2}\hat{\mathbf{A}}\hat{\mathbf{D}}^{-1/2}\mathbf{X}\mathbf{W},$
(1)

where $\hat{\mathbf{A}}=\mathbf{A}+\mathbf{I}$, $\mathbf{I}$ is the identity
matrix and $\hat{\mathbf{D}}$ is the degree matrix of $\hat{\mathbf{A}}$ and
$\mathbf{W}$ is trainable weight matrix of the graph convolutional layer. We
point out that we don’t use any non -linear activations in this module, and
the aggregation operation in Equation (1) can be efficiently carried out using
sparse matrix multiplications. Moreover, we avoid the issue of non-
interpretability in the feature extraction module, which is usually associated
with non-linearities.

As the graph semantics are different for each of the networks, no parameter
sharing is employed at this stage. We use three separate graph convolutional
layers to extract representations for miRNA, PCG and disease nodes. These
learned representation will be fed as input to the multi-task
optimization/learning module explained in the next section.

###  3.3 Multi-task optimization/learning

To effectively utilize information from miRNA-PCG and disease-PCG
interactions, we design a multitask objective to train our model. In
particular, for miRNA-disease, miRNA-PCG, and disease-PCG pairs, we construct
association representations by taking an elementwise product of the
corresponding node features. For example, for an miRNA-disease input pair
$(m,d)$ denoted by nodes $m$ and $d$ respectively, we obtain the corresponding
feature vector representation as

$\mathbf{x}_{md}^{\prime}=\mathbf{X}^{\prime}_{m}\odot{\mathbf{X}}^{\prime}_{m}$

where $\mathbf{X}^{\prime}_{m}$ and $\mathbf{X}^{\prime}_{d}$ correspond to
output representations of the corresponding graph convolution layer for nodes
$m$ and $d$ respectively.

Using the pairwise representations, we then predict the existence of an
association between miRNA and disease; and the confidence score of
associations for miRNA-PCG and disease-PCG pairs. In summary, we train our
model with multi-task loss functions calculated from these three supervised
tasks and use an adaptive loss balancing technique to combine the three
individual loss components dynamically at training time. Details about
individual task loss and our optimization strategy are presented in the
following sections.

####  3.3.1 miRNA-disease binary classification task loss ($\mathcal{L}_{1}$).

We compute the probability of observing an association between an miRNA-
disease input pair $(m,d)$ as:

$\displaystyle
y_{md}=\sigma\left(\mathbf{w}^{T}_{MD}\mathbf{x}^{\prime}_{md}\right)$ (2)

where $\mathbf{w}_{MD}$ is a learnable weight matrix and $\sigma(x)={1\over
1+\exp(-x)}$ is the sigmoid function. We use binary cross entropy to calculate
the training loss for the miRNA-disease classification module as follows:

$\displaystyle\mathcal{L}_{1}=\sum_{m,d}-z_{md}\log
y_{md}-(1-z_{md})\log(1-y_{md})$ (3)

where $z_{md}$ denote the target label known for the corresponding training
pair.

####  3.3.2 miRNA-PCG regression task loss ($\mathcal{L}_{2}$).

For an input miRNA-PCG pair $(m,p)$, we compute the association confidence
score as:

$\displaystyle
y_{mp}=\sigma\left(\mathbf{w}^{T}_{MP}\mathbf{x}^{\prime}_{mp}\right)$ (4)

where $\mathbf{w}_{MP}$ is a learnable weight matrix and $\sigma(x)$ is the
sigmoid function. We use the sum of squared error to calculate the training
loss for the miRNA-PCG regression module as follows:

$\displaystyle\mathcal{L}_{2}=\sum_{m,p}(y_{mp}-z_{mp})^{2},$ (5)

where $z_{mp}$ denotes the target confidence score.

####  3.3.3 disease-PCG regression task loss ($\mathcal{L}_{3}$).

Similar to prediction of miRNA-PCG association, we compute for a disease-PCG
input pair $(d,p)$ the association confidence score, $y_{dp}$.
$\mathcal{L}_{3}$ is then calculated using the sum of squared error as in 5.

$\displaystyle\mathcal{L}_{3}=\sum_{d,p}(y_{dp}-z_{dp})^{2},$ (6)

where $z_{dp}$ denotes the target confidence score.

####  3.3.4 Multi-Task optimization

We define the final loss for our model as the linear combination of three
losses as follows:

$\displaystyle\mathcal{L}=\mathcal{L}_{1}+\alpha_{2}\mathcal{L}_{2}+\alpha_{3}\mathcal{L}_{3},$
(7)

where $\alpha_{2}$, and $\alpha_{3}$ are the loss weight for the two side
tasks. Generally, multi-task networks are difficult to train. Finding the
optimal combination of individual task losses is challenging and problem-
specific. A task that is too dominant during training will overwhelm the
update signals and prevent the network parameters from converging to robust
shared features that are useful across all tasks.

We update $\alpha_{2}$, and $\alpha_{3}$ so that the difference between the
two side tasks contribution at each time step $t$ is minimized. More
specifically, at each time step $t$, the values for $\alpha_{2}$, and
$\alpha_{3}$ are computed dynamically as follows:

$\displaystyle\alpha_{2}(t)=\frac{\mathcal{L}_{3}(t-1)}{\mathcal{L}_{1}(t-1)+\mathcal{L}_{2}(t-1)+\mathcal{L}_{3}(t-1)+10^{-1}}$
(8)
$\displaystyle\alpha_{3}(t)=\frac{\mathcal{L}_{2}(t-1)}{\mathcal{L}_{1}(t-1)+\mathcal{L}_{2}(t-1)+\mathcal{L}_{3}(t-1)+10^{-1}}$
(9)

We use Adam optimizer with a learning rate of $10^{-3}$ to train the multi-
task model.

##  4 Experimental setup

###  4.1 miRNA-disease association datasets

We retrieve the set of miRNA-disease associations from the HMDD v2.0 database
[17] and HMDD v3.0 database [20]. As pre-processing steps, we retain only
associations for miRNAs and diseases for which the PCG interaction information
is available. The filtered data for the HMDD v2.0 database, which from now on
is denoted as Hmdd2, contains 2,303 known associations between 368 miRNA and
124 diseases. The filtered data for the HMDD v3.0 database, which from now on
is referred to as Hmdd3, includes 8,747 known associations between 710 miRNAs
and 311 diseases.

TABLE I: The data statistics where $|E|$, $|V_{miRNA}|$, $|V_{disease}|$ refer to the number of associations/links, miRNAs and diseases respectively. Dataset | $|E|$ | $|V_{miRNA}|$ | $|V_{disease}|$
---|---|---|---
Hmdd2 | 2,303 | 368 | 124
Hmdd3 | 8,747 | 710 | 311

###  4.2 miRNA-PCG interaction.

We obtain the miRNA-PCG interactions from the RAIN database [35]. We include
only interactions for the PCGs with at least one associated Reactome pathway
[36] as these would be biologically more significant. We use all known miRNA-
PCG pairs to train the mi-PCG interaction confidence score prediction task and
the target variables are set to be the normalized confidence score of the
interaction retrieved from the database.

###  4.3 disease-PCG interaction.

We obtain the disease-PCG associations from the DISEASES database [37]. Here
also, we retain only the associations for the PCGs with at least one
associated Reactome pathway. As above, we use all known disease-PCG pairs to
train the disease-PCG interaction confidence score prediction side task and
the target variables are set to be the normalized confidence score of the
known associations retrieved from the database.

Table II provides statistics of the three biological networks as described in
section 3.1 and the two additional datasets described in section 4.2 and 4.3.

TABLE II: Statistics for datasets with side information. $|E|$ is the number of connections/associations. $|V_{m}|$, $|V_{d}|$, and $|V_{P}|$ are the number of miRNAs, diseases, and PCGs, correspondingly. Network | $|E|$ | $|V_{m}|$ | $|V_{d}|$ | $|V_{P}|$
---|---|---|---|---
miRNA-PCG | 178,716 | 714 | - | 9,236
disease-PCG | 144,846 | - | 312 | 9,236
miRNA family ($\mathcal{G}_{m}$) | 1,354 | 217 | - | -
disease ontology ($\mathcal{G}_{d}$) | 90 | - | 128 | -
PCG-PCG ($\mathcal{G}_{p}$) | 4,446,583 | - | - | 9,236

###  4.4 Our new test sets

For small-size datasets like Hmdd2 and Hmdd3, 5-fold CV evaluation is limited
as the size of the training and testing sets become much smaller. While one
can use Hmdd2 for training and Hmdd3 for testing, such evaluation is limited
as there are many overlapping associations in these two datasets. We,
therefore, carefully construct the following four independent tests using the
Hmdd3 dataset. Hmdd2 is used as the training set for evaluation with the new
test sets. Let $\mathbf{M2}$ be the set of all miRNA in Hmdd2 and
$\mathbf{D2}$ be the set of all diseases in Hmdd2. The construction of the
four independent test sets is described below.

TABLE III: Statistics for our new test datasets. Dataset | $|E|$ | $|V_{miRNA}|$ | $|V_{disease}|$
---|---|---|---
Held-out1 | 2,669 | 324 | 110
Held-out2 | 6,641 | 692 | 303
Novel-miRNA | 3,575 | 577 | 115
Novel-Disease | 5,308 | 346 | 295

Held-out1 for transductive testing. To construct our Held-out1 test set we
include all miRNA in $\mathbf{M2}$ and disease nodes in $\mathbf{D2}$ which
are common in Hmdd2 and Hmdd3. The Held-out1 test set contains only the
associations which are present in Hmdd3 but not in Hmdd2. This is a
transductive setting for link/association prediction such that all nodes in
the test set are the same as that in the training set. We randomly generate
the same number of negative samples from the set of unknown miRNA-disease
pairs. Finally, Held-out1 contains 2,669 known associations between 324 miRNAs
and 110 diseases.

Held-out2 for inductive testing. We construct the Held-out2 test set by
including all miRNA and disease nodes and their known associations that are
present in Hmdd3 but not in Hmdd2. Note that different from Held-out1, Held-
out2 might also contain associations corresponding to miRNA and disease nodes
which are not present in the training set Hmdd2. We randomly generate the same
number of negative samples from the set of unknown miRNA-disease pairs. Held-
out2 consists of 6,641 known associations between 692 miRNAs and 303 diseases.

Novel-miRNA. We first fix the set of diseases in our test set to those
retained from Hmdd2, i.e., the set $\mathbf{D2}$ . Then all known associations
that are (i) between diseases in $\mathbf{D2}$ and any miRNA and (ii) present
in Hmdd3 but not in Hmdd2 are included in the Novel-miRNA test set.

We randomly generate the same number of negative samples from the set of
unknown miRNA-disease pairs. In the end, our unseen miRNA testing set consists
of 3,575 associations between 577 miRNAs and 115 diseases. We use this set to
test the performance of models on unseen data where there are new miRNAs for
which no prior association is present in the training set.

Novel-Disease. Similarly, for constructing the Novel-Disease test set, we
first restrict the set of known associations to only those that involve miRNAs
in $\mathbf{M2}$. Then we retain only associations that are in Hmdd3 but not
in Hmdd2. We randomly generate the same number of negative samples from the
set of unknown miRNA-disease pairs. Finally, we attain 5,308 associations
between 346 miRNAs and 295 diseases. We use this set to test the performance
of models on unseen data where there are new diseases for which no prior
association is present in the training set.

A schematic Venn diagram of the four large independent test sets is presented
in Figure 5.

Figure 5: An illustration of the four large independent test sets relations
where Held-out1 = Novel-miRNA $\cap$ Novel-Disease, Held-out2 = Novel-miRNA
$\cup$ Novel-Disease = Hmdd3 \ Hmdd2.

###  4.5 Benchmarked models

We compare our model with five recently proposed supervised methods. Details
about our benchmarked models are given below.

Epmda [12] Epmda consists of two components: the feature extractor and the
classifier. The feature extraction module adopts a network topology-based
approach and operates on a heterogeneous network $G$. $G$ is constructed from
the known miRNA-disease association set, the pre-calculated miRNA, and disease
Gaussian Interaction Profile Kernel (GIP) [38] similarities. A Multiple Layer
Perceptron (MLP) classifier is employed to do the classification task. Epmda
feature extractor and classifier get trained separately.

NEMII [39] NEMII uses Structural Deep Network Embedding (SDNE) to learn the
embedding for miRNA and disease from a bipartite graph built up from known
association information. The learned embedding and features extracted from the
miRNA family and disease semantic similarity are concatenated to form the
input to a Random Forest classifier.

Nimgcn [16] Nimgcn proposes an end-to-end learning framework that operates on
a heterogeneous network $G$, which is built up from miRNA-disease known
associations, the pre-calculated miRNA functional similarity (MISIM), and the
disease semantic similarities. GCN and non-linear transformation layers are
employed to learn the latent representation for miRNAs and diseases
separately. For a particular miRNA-disease pair, its association probability
is calculated as the inner product of the two corresponding latent feature
vectors.

Dbmda [13]. Dbmda separates the feature learning and classification process.
It uses autoencoders to learn the hidden representation for each miRNA-disease
pair from pre-calculated similarities as input. The autoencoders are trained
in an unsupervised manner. The encoded representation will then be fed as
input into a Rotation Forest classifier whose job is to predict potential
miRNA-disease associations.

DimiG 2.0 [15] DimiG 2.0 is a semi-supervised approach that treats miRNA-
disease association prediction as a multiclass classification problem where
diseases are the labels. They do not use miRNA-disease association during the
training process. Instead, they use only the known disease-PCGs interaction to
learn the model parameters. PCGs nodes are connected with miRNAs nodes in a
heterogeneous network based on miRNAs interaction profiles. Learned signals
are then propagated through the heterogeneous network to infer the labels for
miRNAs.

Note that from all the above baselines, only DimiG 2.0 can be used for testing
in inductive settings, i.e., testing for associations/links when the
corresponding nodes are not present in the train set.

###  4.6 Test set-up and evaluation

As in previous works, we perform 5-fold CV for testing on Hmdd2 and Hmdd3
datasets. We run 5-fold CV with 5 random initializations. In other words, for
each dataset, we run each model $5\times 5=25$ times and report the average
performance with the standard deviation.

To test on our new test sets, we train the models on the Hmdd2 dataset. We run
the experiments 5 times with random initializations and report the mean
performance scores along with standard deviation. For case studies, we run
each model with the given case study data 5 times and report average
performance for each model.

Evaluation metrics. Following previous works and recommendations in [19], we
report the Area under the Receiver Operating Characteristic (AUC), and Average
Precision(AP) as our evaluation criteria. For our case studies, we report the
number of known associations found in the top K pairs with the highest
prediction scores returned from the model, where $k$ is from 10 to 100 with a
step of 10.

###  4.7 Hyperparameter settings

####  4.7.1 MuCoMiD

In all experiments, we fix the number of training epochs to 200, the embedding
size and the hidden dimension both to 32. We employ Adam optimizer with a
learning rate of $10^{-3}$ for training.

####  4.7.2 Benchmarked models

For Epmda, Dbmda and Nimgcn, we use the code and setup released in [19]. For
NEMII, we use the same code and setup as published by the authors.

For DimiG 2.0, we use the code and parameters shared by the author. To test
the model performance on our data, we compared DimiG 2.0 with the input
features as tissue expression profiles and DimiG 2.0 with one-hot vectors on
the subset of our testing datasets that have miRNA expression profiles
available. The two models acquire similar performance. This implies that use
of tissue expression profiles as node features do not have an effect on the
model performance. We can therefore the test the model on data for which
tissue expression information is unavailable by using one hot encoding for
input node features. The results reported in Section 5 corresponds to the
model with one-hot vectors as input features on our testing datasets without
removing miRNAs that don’t have expression profiles.

##  5 Results

In the following, we discuss the performance gains of our approach (and their
implications) on a variety of testing scenarios and new test datasets
described in sections 4.1 and 4.4.

###  5.1 Results on small test sets

TABLE IV: Results with 5FoldCV over the Hmdd2 and Hmdd3 datasets. We perform 5-Fold CV 5 times with different random seeds. We report the average over 25 scores along with standard deviation. Our improvements over state-of-the-art (SOTA) methods are statistically significant with a p-value less than 0.002 on Hmdd2 and less than $10^{-30}$ on Hmdd3 datasets. Method | HMDD2 | HMDD3
---|---|---
| AUC | AP | AUC | AP
Epmda ([12]) | 0.744 $\pm$ 0.019 | 0.783 $\pm$ .017 | 0.520 $\pm$ 0.011 | 0.594 $\pm$ 0.010
Nimgcn ([16]) | 0.785 $\pm$ 0.018 | 0.803 $\pm$ 0.015 | 0.795 $\pm$ 0.021 | 0.800 $\pm$ 0.018
Dbmda ([13]) | 0.553 $\pm$ 0.019 | 0.537 $\pm$ 0.015 | 0.749 $\pm$ 0.010 | 0.696 $\pm$ 0.009
NEMII ([39]) | 0.499 $\pm$ 0.019 | 0.502 $\pm$ 0.020 | 0.512 $\pm$ 0.008 | 0.509 $\pm$ 0.006
DimiG 2.0 ([15]) | 0.493 $\pm$ 0.018 | 0.485 $\pm$ 0.012 | 0.516 $\pm$ 0.006 | 0.508 $\pm$ 0.005
MuCoMiD (ours) | 0.833 $\pm$ 0.012 | 0.832 $\pm$ 0.015 | 0.915 $\pm$ 0.004 | 0.910 $\pm$ 0.006
Improvement over SOTA | 6.1% | 3.6% | 15.1% | 13.8%

Following previous works, we perform 5-fold CV experiments on Hmdd2 and Hmdd3
datasets. The results are shown in Table IV. The test set size for this
scenario is considerably smaller and contains only $1/5$th of the total
associations. The test size is then considerably smaller for datasets like
Hmdd2. Such a train-test scenario allows us to quantify how well the models
learn but is limited in testing the generalization power of the models.

While the graph-based approaches Epmda and Nimgcn perform reasonably well in
this scenario, our multi-task-based approaches significantly supersede state-
of-the-art (SOTA) methods with an improvement of up to $\sim$13.8% in AP
score.

The performance of Epmda drops considerably for Hmdd3 dataset. Epmda learns
edge features in an unsupervised manner corresponding to its contribution to a
cycle of a particular length. Usually, the cycle length parameter is fixed to
a small value due to an exponential increase in run time with an increase in
cycle length. Moreover, the task signal is not used in learning the edge
features. The loss of performance of Epmda in Hmdd3 can be attributed to the
limitation of finding the best cycle length hyperparameter applicable for
Hmdd3. This also limits the applicability of this model to a larger variety of
datasets. Nimgcn performs better than Dbmda due to the higher representational
capacity of used GCNs and exploitation of additional graph structure.

###  5.2 Results on small train but larger test sets in transductive setting

TABLE V: Results on the Held-out1 dataset after five runs. Our improvements over SOTA methods are statistically significant with a p-value less than $10^{-15}$. Method | AUC | AP
---|---|---
Epmda | 0.562 $\pm$ 0.085 | 0.511 $\pm$ 0.064
Nimgcn | 0.676 $\pm$ 0.009 | 0.642 $\pm$ 0.006
Dbmda | 0.545 $\pm$ 0.011 | 0.521 $\pm$ 0.009
NEMII | 0.504 $\pm$ 0.002 | 0.501 $\pm$ 0.000
DimiG 2.0 | 0.429 $\pm$ 0.002 | 0.471 $\pm$ 0.003
MuCoMiD (ours) | 0.705 $\pm$ 0.005 | 0.691 $\pm$ 0.006
Improvement over SOTA | 4.3% | 7.6%

Table V shows results corresponding to Held-out1 test set. Recall that Hmdd2
is used as the training set. In this scenario, the test size is much larger
than the train set size allowing us to compare the generalization capability
of the models.

Out of the state-of-the-art models, Nimgcn is the best owing to the use of
GCNs to extract latent representations. The overall drop in performance of all
models in this scenario as compared to small test size case points to the
hardness of this particular test set. Existing methods show a bad
generalization capability.

Though both MuCoMiD and Nimgcn employ GCNs to extract latent representations,
we still observe a significant difference between the two models’ performance.
The reason for such difference are: (i) Nimgcn is a single task-based model
which relies on pre-calculated secondary features and learns solely from the
set of known associations while (ii) MuCoMiD is a multitask-based model which
can incorporate information from five additional knowledge sources to learn
the feature automatically at run time and to overcome the limited training
data problem.

###  5.3 Results on small train but larger test sets in inductive setting

TABLE VI: Results on large test sets with new miRNA and disease after five runs. Our improvements over SOTA methods are statistically significant with a p-value less than $10^{-15}$. All models are trained on Hmdd2 dataset. Method | Novel-miRNA | Novel-Disease | Held-out2
---|---|---|---
| AUC | AP | AUC | AP | AUC | AP
DimiG 2.0 | 0.452 $\pm$ 0.001 | 0.480 $\pm$ 0.001 | 0.421 $\pm$ 0.001 | 0.467 $\pm$ 0.001 | 0.417 $\pm$ 0.003 | 0.465 $\pm$ 0.004
MuCoMiD | 0.726 $\pm$ 0.005 | 0.713 $\pm$ 0.006 | 0.637 $\pm$ 0.019 | 0.632 $\pm$ 0.025 | 0.648 $\pm$ 0.019 | 0.662 $\pm$ 0.025
Improvement over SOTA | 60.6% | 48.5% | 51.3% | 35.3% | 55.4% | 42.4%

Table VI shows results corresponding to testing on Held-out2 dataset and
training on Hmdd2. Note that Held-out2 is more than three times larger than
the training data and contains new nodes that have not been seen in Hmdd2.
Epmda, Dbmda, Nimgcn, and NEMII rely on the known miRNA-disease associations
to calculate similarities or learn structural embedding for miRNA and disease.
Therefore, they cannot be compared in the current inductive setting with new
miRNAs or new diseases.

For such large testing set with many new nodes, MuCoMiD still claims its
superior performance. With resepct to AP score, MuCoMiD shows an improvement
of $\sim$42.4% over DimiG 2.0.

This dataset further supports the importance of the chosen architecture and
the effectiveness of multi-sources data integration.

###  5.4 Results on Novel-Disease and Novel-miRNA datasets

Table VI shows results with Novel-Disease and Novel-miRNA test sets. Epmda,
Dbmda, Nimgcn, and NEMII cannot be used for inductive setting with new nodes.

MuCoMiD again outperforms its competitor, the DimiG 2.0 model. The gain is
more significant on Novel-miRNA datasets with a gain of $\sim 48.5\%$ in AP
score. On Novel-Disease test set, MuCoMiD is $\sim$35.3% better in AP score
than DimiG 2.0.

MuCoMiD and DimiG 2.0 share some design similarities as (i) both use GCNs to
learn the node representation from the input graph (ii) both incorporate
information from the disease ontology, miRNA-PCG, disease-PCG and PCG-PCG
interaction information. However, there are major differences among the two
approaches which dictate the differences in their performance. Firstly,
MuCoMiD use multiple GCNs to learn the representations for miRNA, disease, and
PCG separately from different input graphs while DimiG 2.0 has a single GCN
layer to learn the representation for miRNA and PCG from a network build up
from PCG-PCG and miRNA-PCG interactions while ignoring the heterogeneous
character of the network. Secondly,DimiG 2.0 formulates the miRNA-disease
association prediction as a multi-label node classification problem and DimiG
2.0 does not use known miRNA-disease associations to train the model; while
MuCoMiD is a supervised multitask model that formulate the miRNA-disease
association prediction as a binary classification problem.

Finally, a major difference between the two methods is that DimiG 2.0 can only
work for miRNA and disease for which there exist at least one known
association with any PCG. MuCoMiD does not suffer from such a problem and can
work for any set miRNA and diseases.

###  5.5 Ablation study

We conduct ablation study to analyze the contribution of additional tasks. The
single task baseline employs similar architecture as that of MuCoMiD but
without the miRNA-PCG and disease-PCG interaction confidence score prediction
side tasks. In other words, it also learns miRNA and disease representation
from the miRNA family and the disease ontology networks, respectively.
However, they only have one classifier layer for the miRNA-disease association
prediction task, instead of one classifier and two regressors as that of
MuCoMiD.

Table VII presents the results for MuCoMiD and its single-task variant on both
5-FoldCV and independent testing set up. MuCoMiD consistently out-perform its
single-task baseline in all datasets. The difference is more significant on
the large independent testing sets, especially in Held-out1 and Novel-miRNA.
These improvements account for the contribution of the two added side tasks.
Since PCGs are the most important link between miRNAs and their associated
diseases [30], miRNA-PCG and disease-PCG prediction tasks also bring
additional insights into the miRNA-disease association prediction problem.

TABLE VII: Ablation study results for both 5-FoldCV and independent testing set up. Method | MuCoMiD | Single task MuCoMiD
---|---|---
| AUC | AP | AUC | AP
Hmdd2 |  0.833 $\pm$ 0.012 |  0.832 $\pm$ .015 | 0.827 $\pm$ 0.009 | 0.826 $\pm$ 0.011
Hmdd3 |  0.915 $\pm$ 0.004 |  0.910 $\pm$ 0.006 | 0.911 $\pm$ 0.004 | 0.906 $\pm$ 0.006
Held-out1 |  0.705 $\pm$ 0.005 |  0.691 $\pm$ 0.006 | 0.667 $\pm$ 0.008 | 0.659 $\pm$ 0.007
Novel-miRNA |  0.726 $\pm$ 0.005 |  0.714 $\pm$ 0.006 | 0.671 $\pm$ 0.008 | 0.678 $\pm$ 0.006
Novel-Disease |  0.637 $\pm$ 0.019 |  0.632 $\pm$ 0.025 | 0.603 $\pm$ 0.015 | 0.616 $\pm$ 0.001
Held-out2 |  0.648 $\pm$ 0.019 |  0.662 $\pm$ 0.025 | 0.603 $\pm$ 0.015 | 0.653 $\pm$ 0.010

##  6 Case studies

To further demonstrate our multi-task model’s predictive capability, we
evaluate the model for three specific diseases: BreastCancer,
PancreaticCancer, and DiabetesMellitus-type 2. By constructing these case
studies, we showcase our model’s predictive performance in predicting
associations for a specific new disease. To do that, for a specific disease
$d$, we select the pairs associated with $d$ from the Hmdd3 to use as the
testing set. The remaining pairs in Hmdd3 are used as the training data. We do
negative sampling for both the training and testing set so that the number of
positive and negative samples in both training and testing sets are equal. For
the test set, the negative pairs are generated only corresponding to the
disease $d$. The dataset statistics for our case studies are presented in
table VIII where we use the disease names to denotes our generated dataset for
that particular disease case study.

TABLE VIII: Our case studies’ datasets’ statistics, where $n^{+}$ and $n^{-}$ refer to the number of positive and negative associations/links, respectively. Disease | Train Samples | Train Samples
---|---|---
| $n^{+}$ | $n^{-}$ | $n^{+}$ | $n^{-}$
BreastCancer | 8423 | 8423 | 324 | 324
PancreaticCancer | 8578 | 8578 | 169 | 169
DiabetesMellitus | 8640 | 8640 | 107 | 107
Figure 6: topK interactions for case studies. Mean number of interactions found over 5 independent runs of the models are reported. TABLE IX: MicroRNAs predicted corresponding to top 50 highest prediction scores produced by MuCoMiD (average after 5 runs) for the three case studies. No. | BreastCancer | PancreaticCancer |  DiabetesMellitus-type 2
---|---|---|---
1. | hsa-mir-21 | 0.955 | hsa-mir-21 | 0.956 | hsa-mir-21 | 0.958
2. | hsa-mir-155 | 0.943 | hsa-mir-146a | 0.941 | hsa-mir-155 | 0.951
3. | hsa-mir-146a | 0.938 | hsa-mir-155 | 0.936 | hsa-mir-146a | 0.947
4. | hsa-mir-145 | 0.931 | hsa-mir-146b | 0.933 | hsa-mir-29b | 0.938
5. | hsa-mir-29b | 0.925 | hsa-mir-29b | 0.912 | hsa-mir-145 | 0.935
6. | hsa-mir-146b | 0.921 | hsa-mir-126 | 0.911 | hsa-mir-126 | 0.918
7. | hsa-mir-126 | 0.915 | hsa-mir-145 | 0.907 | hsa-mir-34a | 0.911
8. | hsa-mir-34b | 0.89 | hsa-mir-210 | 0.872 | hsa-mir-142 | 0.903
9. | hsa-mir-150 | 0.889 | hsa-mir-150 | 0.871 | hsa-mir-221 | 0.902
10. | hsa-mir-34a | 0.887 | hsa-mir-34a | 0.87 | hsa-mir-150 | 0.897
11. | hsa-mir-142 | 0.885 | hsa-mir-142 | 0.867 | hsa-mir-34c | 0.896
12. | hsa-mir-34c | 0.882 | hsa-mir-223 | 0.862 | hsa-mir-223 | 0.889
13. | hsa-mir-210 | 0.881 | hsa-mir-34c | 0.857 | hsa-mir-122 | 0.886
14. | hsa-mir-223 | 0.87 | hsa-mir-34b | 0.856 | hsa-mir-222 | 0.869
15. | hsa-mir-221 | 0.868 | hsa-mir-122 | 0.835 | hsa-mir-26a | 0.864
16. | hsa-mir-122 | 0.862 | hsa-mir-206 | 0.83 | hsa-mir-205 | 0.862
17. | hsa-mir-205 | 0.859 | hsa-mir-221 | 0.813 | hsa-mir-29a | 0.853
18. | hsa-mir-31 | 0.857 | hsa-mir-31 | 0.811 | hsa-mir-375 | 0.85
19. | hsa-mir-206 | 0.835 | hsa-mir-143 | 0.809 | hsa-mir-144 | 0.848
20. | hsa-mir-144 | 0.832 | hsa-mir-205 | 0.805 | hsa-mir-143 | 0.846
21. | hsa-mir-29a | 0.832 | hsa-mir-375 | 0.802 | hsa-mir-206 | 0.844
22. | hsa-mir-143 | 0.83 | hsa-mir-222 | 0.795 | hsa-mir-27b | 0.831
23. | hsa-mir-124 | 0.828 | hsa-mir-26a | 0.794 | hsa-mir-24 | 0.83
24. | hsa-mir-26a | 0.827 | hsa-mir-183 | 0.785 | hsa-mir-192 | 0.826
25. | hsa-mir-222 | 0.822 | hsa-mir-181a | 0.785 | hsa-mir-183 | 0.824
26. | hsa-mir-375 | 0.821 | hsa-mir-24 | 0.782 | hsa-mir-27a | 0.819
27. | hsa-mir-183 | 0.818 | hsa-mir-29a | 0.776 | hsa-mir-19b | 0.816
28. | hsa-mir-24 | 0.811 | hsa-mir-22 | 0.748 | hsa-mir-1 | 0.813
29. | hsa-mir-132 | 0.807 | hsa-mir-19a | 0.743 | hsa-mir-214 | 0.808
30. | hsa-mir-1 | 0.797 | hsa-mir-20a | 0.742 | hsa-mir-9 | 0.807
31. | hsa-mir-192 | 0.796 | hsa-mir-204 | 0.74 | hsa-mir-19a | 0.802
32. | hsa-mir-19b | 0.795 | hsa-mir-29b-1 | 0.738 | hsa-mir-17 | 0.798
33. | hsa-mir-29b-1 | 0.793 | hsa-mir-106b | 0.735 | hsa-mir-20a | 0.798
34. | hsa-mir-29c | 0.792 | hsa-mir-29c | 0.735 | hsa-mir-92a | 0.797
35. | hsa-mir-181a | 0.79 | hsa-mir-17 | 0.734 | hsa-mir-22 | 0.797
36. | hsa-mir-27b | 0.789 | hsa-mir-106a | 0.734 | hsa-mir-93 | 0.795
37. | hsa-mir-196a | 0.785 | hsa-mir-18a | 0.732 | hsa-mir-182 | 0.792
38. | hsa-mir-182 | 0.78 | hsa-mir-132 | 0.727 | hsa-mir-18a | 0.792
39. | hsa-mir-19a | 0.78 | hsa-mir-182 | 0.722 | hsa-mir-132 | 0.791
40. | hsa-mir-22 | 0.779 | hsa-mir-18b | 0.717 | hsa-mir-18b | 0.788
41. | hsa-mir-17 | 0.777 | hsa-mir-19b-1 | 0.705 | hsa-mir-20b | 0.785
42. | hsa-mir-20a | 0.777 | hsa-mir-27b | 0.698 | hsa-mir-23b | 0.785
43. | hsa-mir-9 | 0.776 | hsa-mir-16 | 0.688 | hsa-mir-486 | 0.782
44. | hsa-mir-92a | 0.774 | hsa-mir-15a | 0.688 | hsa-mir-15a | 0.775
45. | hsa-mir-15a | 0.773 | hsa-mir-92a | 0.686 | hsa-mir-195 | 0.771
46. | hsa-mir-214 | 0.772 | hsa-mir-342 | 0.683 | hsa-mir-204 | 0.767
47. | hsa-mir-106b | 0.771 | hsa-mir-214 | 0.682 | hsa-mir-320a | 0.766
48. | hsa-mir-29b-2 | 0.769 | hsa-mir-133b | 0.68 | hsa-mir-23a | 0.757
49. | hsa-mir-16 | 0.769 | hsa-mir-9 | 0.678 | hsa-mir-15b | 0.749
50. | hsa-mir-93 | 0.768 | hsa-mir-96 | 0.673 | hsa-mir-133b | 0.749

Figure 6 presents the topK evaluation results while table IX shows the top 50
predictions of the associated miRNAs for the three diseases. For each case
study, the known association for that particular disease is completely hidden
from the model training process. The statistics of our training and testing
data can be found in Section VIII.

Looking at Figure 6, though the case studies’ diseases are completely new, our
model still attains near-perfect prediction for the top 40 predictions.
Compared with DimiG 2.0 for the top 50 predictions, our model has a gain of at
least $166\%$.

For BreastCancer, the model acquires $\sim 97.6\%$ accuracy for the top 100
predictions. For DiabetesMellitus-type 2, the number of known positive
associations is only 107, but out of the top 100 predictions, we can correctly
recognize $92.4$ associations (this number is the average of the number of
found interactions over five runs). These results again claim the
effectiveness of our model in predicting potential associations for new
diseases.

##  7 Conclusion

We propose a multi-task graph convolutional learning framework, MuCoMiD for
the problem of predicting miRNA-disease associations. Our end-to-end learning
approach allows automatic feature extraction while incorporating knowledge
from five heterogeneous biological information sources. Incorporating many
sources of information helps compensate for the lack of information in any
single source and, at the same time, enables the model to generate predictions
for any new miRNA or disease. Unlike previous works, our model can be employed
in both transductive and inductive settings. To test the generalization power
of models, we construct and test on both existing benchmarked set up and on
larger independent test sets. Large-scale experiments in several testing
scenarios highlight the superiority of our approach. An ablation study is
added to highlight the side tasks’ contribution. We release all the code and
data used in this study for reproducibility and future research at
https://git.l3s.uni-hannover.de/dong/cmtt.

We believe that our design principles will be of independent interest for
other biomedical applications where data scarcity is a major challenge. In
particular, the use of multi-task learning to integrate information from
heterogeneous information sources to overcome the problems of data scarcity
and unreliability of one single data type is a unique perspective and has not
been studied for computational problems in biomedicine.

## Acknowledgments

The work is partially supported by the PRESENt project funded by Volkswagen
Stiftung and the State Government of Lower Saxony (grant no. 11-76251-99-3/19
(ZN3434)), the Federal Ministry of Education and Research (BMBF), Germany
under the LeibnizKILabor project (grant no. 01DD20003), and the strategic
funds of the Translational Alliance in Lower Saxony.

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|  Ngan Dong is PhD student at L3S Research Center, Leibniz University of
Hannover, Germany. She works as a research assistant in the PRESENt
project111http://www.translationsallianz.de/train-platforms/train-
projects/present/?L=1, which aims at integrating clinical, biological, and big
data research to advance our understanding of norovirus gastroenteritis. Her
current research focuses on network analysis, feature selection, graph-based
representation learning, multi-task models, and data integration.
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|  Stefanie Mücke received her B. Sc. and M. Sc. in Biology at the Martin-
Luther-Universität Halle/Wittenberg (Germany) in 2013 and 2016, respectively.
In 2020, she earned her Ph.D. in Biotechnology at the Gottfried Wilhelm
Leibniz Universität Hannover (Germany), working on host-pathogen interactions,
omics analyses, genetic engineering, and biotechnology. Since 2020, she has
been a scientific officer for the Translational Alliance in Lower Saxony, a
network of 10 research institutions in Hannover and Braunschweig (Germany).
She supports cooperation projects between these institutions in the capacity
of a project manager and scientific advisor.
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|  Megha Khosla received her PhD degree in Theoretical Computer Science from
Max Planck Institute of Informatics and Saarland University, Saarbruecken,
Germany. Currently she is a senior researcher at L3S Research center. Her main
research focus is on effective, interpretable and privacy-preserving learning
on graphs with applications to personalized medicine.
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