RSC Advances,
Год журнала:
2019,
Номер
9(57), С. 33222 - 33228
Опубликована: Янв. 1, 2019
A
growing
body
of
evidence
indicates
that
circular
RNAs
(circRNAs)
play
a
pivotal
role
in
various
biological
processes
and
have
close
association
with
the
initiation
progression
diseases.
Moreover,
circRNAs
are
considered
as
promising
biomarkers
for
disease
diagnosis
owing
to
their
characteristics
conservation,
stability
universality.
Inferring
disease-circRNA
relationships
will
contribute
understanding
pathology.
However,
it
is
costly
laborious
discover
novel
interactions
by
wet-lab
experiments,
few
computational
methods
been
devoted
predicting
potential
Here,
we
advance
method
(NCPCDA)
identify
circRNA-disease
associations
based
on
network
consistency
projection.
For
starters,
make
use
multi-view
similarity
data,
including
circRNA
functional
similarity,
semantic
profile
construct
integrated
similarity.
Then,
project
space
interaction
network,
respectively.
Finally,
can
obtain
predicted
score
matrix
combining
above
two
projection
scores.
Simulation
results
show
NCPCDA
efficiently
infer
high
accuracy,
obtaining
AUCs
0.9541
0.9201
leave-one-out
cross
validation
five-fold
validation,
Furthermore,
case
studies
also
suggest
discovering
new
interactions.
The
dataset
code,
well
detailed
readme
file
our
be
downloaded
from
Github
(https://github.com/ghli16/NNCPCD).
IEEE Transactions on Cybernetics,
Год журнала:
2021,
Номер
53(1), С. 67 - 75
Опубликована: Июль 8, 2021
Clinical
evidence
began
to
accumulate,
suggesting
that
circRNAs
can
be
novel
therapeutic
targets
for
various
diseases
and
play
a
critical
role
in
human
health.
However,
limited
by
the
complex
mechanism
of
circRNA,
it
is
difficult
quickly
large-scale
explore
relationship
between
disease
circRNA
wet-lab
experiment.
In
this
work,
we
design
new
computational
model
MGRCDA
on
account
metagraph
recommendation
theory
predict
potential
circRNA-disease
associations.
Specifically,
first
regard
association
prediction
problem
as
system
problem,
series
metagraphs
according
heterogeneous
biological
networks;
then
extract
semantic
information
Gaussian
interaction
profile
kernel
(GIPK)
similarity
network
attributes;
finally,
iterative
search
algorithm
used
calculate
scores
pair.
On
gold
standard
dataset
circR2Disease,
achieved
accuracy
92.49%
with
an
area
under
ROC
curve
0.9298,
which
significantly
higher
than
other
state-of-the-art
models.
Furthermore,
among
top
30
disease-related
recommended
model,
25
have
been
verified
latest
published
literature.
The
experimental
results
prove
feasible
efficient,
recommend
reliable
candidates
further
experiment
reduce
scope
IEEE Journal of Biomedical and Health Informatics,
Год журнала:
2023,
Номер
27(6), С. 3072 - 3082
Опубликована: Март 23, 2023
Exploring
the
relationship
between
circular
RNA
(circRNA)
and
disease
is
beneficial
for
revealing
mechanisms
of
pathogenesis.
However,
a
blind
search
all
possible
associations
circRNAs
diseases
through
biological
experiments
time-consuming.
Although
some
prediction
methods
have
been
proposed,
they
still
limitations.
In
this
study,
novel
computational
framework,
called
GATCL2CD,
proposed
to
forecast
unknown
circRNA-disease
(CDAs).
First,
we
calculate
Gaussian
interactive
profile
kernel
(GIP)
similarity
semantic
diseases,
circRNA
sequence
function
similarity,
GIPs
circRNAs.
Then,
combine
them
construct
heterogeneous
graph.
Thereafter,
GATCL2CD
proposes
feature
convolution
learning
that
uses
multi-head
dynamic
attention
mechanism
obtain
different
aggregated
representations
features
correspond
nodes
in
it
extracts
rich
higher-order
from
stacked
each
node
by
using
single-layer
convolutional
neural
network
with
filter
kernels
sizes.
Finally,
pairwise
element-wise
product
operation
implemented
capture
interactions
representations,
multilayer
perceptron
introduced
as
an
efficient
classifier
inferring
potential
CDAs.
Major
experimental
results
under
5-fold
cross-validation
(5-fold
CV)
on
three
datasets
show
superior
five
other
state-of-the-art
methods.
Furthermore,
case
studies
demonstrate
suitability
useful
tool
identifying
disease-related
Abstract
Background
Circular
RNAs
(circRNAs)
have
been
confirmed
to
play
a
vital
role
in
the
occurrence
and
development
of
diseases.
Exploring
relationship
between
circRNAs
diseases
is
far-reaching
significance
for
studying
etiopathogenesis
treating
To
this
end,
based
on
graph
Markov
neural
network
algorithm
(GMNN)
constructed
our
previous
work
GMNN2CD,
we
further
considered
multisource
biological
data
that
affects
association
circRNA
disease
developed
an
updated
web
server
CircDA
human
hepatocellular
carcinoma
(HCC)
tissue
verify
prediction
results
CircDA.
Results
built
Tumarkov-based
deep
learning
framework.
The
regards
biomolecules
as
nodes
interactions
molecules
edges,
reasonably
abstracts
multiomics
data,
models
them
heterogeneous
biomolecular
network,
which
can
reflect
complex
different
biomolecules.
Case
studies
using
literature
from
HCC,
cervical,
gastric
cancers
demonstrate
predictor
identify
missing
associations
known
diseases,
quantitative
real-time
PCR
(RT-qPCR)
experiment
HCC
samples,
it
was
found
five
were
significantly
differentially
expressed,
proved
predict
related
new
circRNAs.
Conclusions
This
efficient
computational
case
analysis
with
sufficient
feedback
allows
us
circRNA-associated
disease-associated
Our
provides
method
provide
guidance
certain
For
ease
use,
online
(
http://server.malab.cn/CircDA
)
provided,
code
open-sourced
https://github.com/nmt315320/CircDA.git
convenience
improvement.
Journal of Chemical Information and Modeling,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 12, 2025
In
the
emerging
field
of
RNA
drugs,
circular
(circRNA)
has
attracted
much
attention
as
a
novel
multifunctional
therapeutic
target.
Delving
deeper
into
intricate
interactions
between
circRNA
and
disease
is
critical
for
driving
drug
discovery
efforts
centered
around
circRNAs.
Current
computational
methods
face
two
significant
limitations:
lack
aggregate
information
in
heterogeneous
graph
networks
higher-order
fusion
information.
To
this
end,
we
present
approach,
metaCDA,
which
utilizes
meta-knowledge
adaptive
learning
to
improve
accuracy
association
predictions
addresses
limitations
both.
We
calculate
multiple
similarity
measures
circRNA,
construct
based
on
these,
apply
meta-networks
extract
from
graph,
so
that
constructed
maps
have
contrast
enhancement
Then,
nodal
aggregation
system,
integrates
multihead
mechanism
mechanism,
achieve
accurate
capture
conducted
extensive
experiments,
results
show
metaCDA
outperforms
existing
state-of-the-art
models
can
effectively
predict
disease-associated
opening
up
new
prospects
circRNA-driven
discovery.
IEEE Access,
Год журнала:
2019,
Номер
7, С. 83474 - 83483
Опубликована: Янв. 1, 2019
Identification
of
circRNA-disease
associations
provides
insight
into
the
mechanism
that
circRNAs
cause
diseases.
Wet
experimental
identification
is
time-consuming
and
labor-intensive,
thus
developing
computational
methods
for
association
prediction
an
urgent
task.
In
this
paper,
we
propose
a
linear
neighborhood
label
propagation
method
to
predict
associations,
named
CD-LNLP.
First,
CD-LNLP
uses
profiles
based
on
known
calculate
circRNA-circRNA
similarities
disease-disease
similarities.
Next,
implements
similarity-based
graph
respectively
associations.
Finally,
combine
outputs
from
model
produce
results.
experiments,
achieves
impressive
performance
with
AUPR
score
0.4487
AUC
0.9007
outperforms
outstanding
baseline
(collaborative
filter
method,
KATZ,
nonnegative
matrix
factorization
resource
allocation
method)
state-of-the-art
MRLDC.
The
case
studies
show
identifies
novel
which
are
validated
by
up-to-date
databases
literature
respectively.
conclusion,
promising
predicting
Scientific Reports,
Год журнала:
2020,
Номер
10(1)
Опубликована: Фев. 6, 2020
Abstract
CircRNA
is
a
special
type
of
non-coding
RNA,
which
closely
related
to
the
occurrence
and
development
many
complex
human
diseases.
However,
it
time-consuming
expensive
determine
circRNA-disease
associations
through
experimental
methods.
Therefore,
based
on
existing
databases,
we
propose
method
named
RWRKNN,
integrates
random
walk
with
restart
(RWR)
k-nearest
neighbors
(KNN)
predict
between
circRNAs
Specifically,
apply
RWR
algorithm
weighting
features
global
network
topology
information,
employ
KNN
classify
features.
Finally,
prediction
scores
each
pair
are
obtained.
As
demonstrated
by
leave-one-out,
5-fold
cross-validation
10-fold
cross-validation,
RWRKNN
achieves
AUC
values
0.9297,
0.9333
0.9261,
respectively.
And
case
studies
show
that
predicted
can
be
successfully
demonstrated.
In
conclusion,
useful
for
predicting
associations.
Briefings in Bioinformatics,
Год журнала:
2020,
Номер
22(4)
Опубликована: Ноя. 2, 2020
The
studies
on
relationships
between
non-coding
RNAs
and
diseases
are
widely
carried
out
in
recent
years.
A
large
number
of
experimental
methods
technologies
producing
biological
data
have
also
been
developed.
However,
due
to
their
high
labor
cost
production
time,
nowadays,
calculation-based
methods,
especially
machine
learning
deep
received
a
lot
attention
used
commonly
solve
these
problems.
From
computational
point
view,
this
survey
mainly
introduces
three
common
RNAs,
i.e.
miRNAs,
lncRNAs
circRNAs,
the
related
for
predicting
association
with
diseases.
First,
mainstream
databases
above
introduced
detail.
Then,
we
present
several
RNA
similarity
disease
calculations.
Later,
investigate
ncRNA-disease
prediction
details
classify
into
five
types:
network
propagating,
recommend
system,
matrix
completion,
learning.
Furthermore,
provide
summary
applications
types
associations
respectively.
Finally,
advantages
limitations
various
identified,
future
researches
challenges
discussed.
IEEE/ACM Transactions on Computational Biology and Bioinformatics,
Год журнала:
2020,
Номер
19(1), С. 168 - 179
Опубликована: Апрель 16, 2020
A
drug-drug
interaction
(DDI)
is
defined
as
an
association
between
two
drugs
where
the
pharmacological
effects
of
a
drug
are
influenced
by
another
drug.
Positive
DDIs
can
usually
improve
therapeutic
patients,
but
negative
cause
major
adverse
reactions
and
even
result
in
withdrawal
from
market
patient
death.
Therefore,
identifying
has
become
key
component
development
disease
treatment.
In
this
study,
we
propose
novel
method
to
predict
based
on
integrated
similarity
semi-supervised
learning
(DDI-IS-SL).
DDI-IS-SL
integrates
chemical,
biological
phenotype
data
calculate
feature
with
cosine
method.
The
Gaussian
Interaction
Profile
kernel
also
calculated
known
DDIs.
(the
Regularized
Least
Squares
classifier)
used
possibility
scores
pairs.
terms
5-fold
cross
validation,
10-fold
validation
de
novo
achieve
better
prediction
performance
than
other
comparative
methods.
addition,
average
computation
time
shorter
that
Finally,
case
studies
further
demonstrate
practical
applications.
Briefings in Bioinformatics,
Год журнала:
2021,
Номер
22(5)
Опубликована: Март 16, 2021
Emerging
research
shows
that
circular
RNA
(circRNA)
plays
a
crucial
role
in
the
diagnosis,
occurrence
and
prognosis
of
complex
human
diseases.
Compared
with
traditional
biological
experiments,
computational
method
fusing
multi-source
data
to
identify
association
between
circRNA
disease
can
effectively
reduce
cost
save
time.
Considering
limitations
existing
models,
we
propose
semi-supervised
generative
adversarial
network
(GAN)
model
SGANRDA
for
predicting
circRNA-disease
association.
This
first
fused
natural
language
features
sequence
semantics,
Gaussian
interaction
profile
kernel,
then
used
all
pairs
pre-train
GAN
network,
fine-tune
parameters
through
labeled
samples.
Finally,
extreme
learning
machine
classifier
is
employed
obtain
prediction
result.
previous
supervision
model,
innovatively
introduced
sequences
utilized
information
during
pre-training
process.
step
increase
content
feature
some
extent
impact
too
few
known
associations
on
performance.
obtained
AUC
scores
0.9411
0.9223
leave-one-out
cross-validation
5-fold
cross-validation,
respectively.
Prediction
results
benchmark
dataset
show
outperforms
other
models.
In
addition,
25
top
30
highest
case
studies
were
verified
by
recent
literature.
These
experimental
demonstrate
useful
predict
provide
reliable
candidates
experiments.
Genomics Proteomics & Bioinformatics,
Год журнала:
2021,
Номер
20(3), С. 435 - 445
Опубликована: Ноя. 29, 2021
Abstract
With
accumulating
dysregulated
circular
RNAs
(circRNAs)
in
pathological
processes,
the
regulatory
functions
of
circRNAs,
especially
circRNAs
as
microRNA
(miRNA)
sponges
and
their
interactions
with
RNA-binding
proteins
(RBPs),
have
been
widely
validated.
However,
collected
information
on
experimentally
validated
circRNA–disease
associations
is
only
preliminary.
Therefore,
an
updated
CircR2Disease
database
providing
a
comprehensive
resource
web
tool
to
clarify
relationships
between
diseases
diverse
species
necessary.
Here,
we
present
v2.0
increased
number
novel
characteristics.
provides
more
than
5-fold
compared
its
previous
version.
This
version
includes
4201
entries
3077
312
disease
subtypes.
Secondly,
circRNA–miRNA,
circRNA–miRNA–target,
circRNA–RBP
has
manually
for
various
diseases.
Thirdly,
gene
symbols
name
IDs
can
be
linked
nomenclature
databases.
Detailed
descriptions
such
samples
journals
also
integrated
into
Thus,
serve
platform
users
systematically
investigate
roles
further
explore
posttranscriptional
function
Finally,
propose
computational
method
named
circDis
based
graph
convolutional
network
(GCN)
gradient
boosting
decision
tree
(GBDT)
illustrate
applications
database.
available
at
http://bioinfo.snnu.edu.cn/CircR2Disease_v2.0
https://github.com/bioinforlab/CircR2Disease-v2.0.