npj Systems Biology and Applications,
Год журнала:
2025,
Номер
11(1)
Опубликована: Апрель 18, 2025
Integrating
biological
data
with
in
silico
modeling
offers
the
transformative
potential
to
develop
virtual
human
models,
or
"digital
twins."
These
models
hold
immense
promise
for
deepening
our
understanding
of
diseases
and
uncovering
new
therapeutic
strategies.
This
approach
is
especially
valuable
lacking
reliable
models.
Here
we
review
current
modelling
efforts
lung
development,
highlighting
role
interdisciplinary
collaboration
key
advances
toward
a
digital
twin.
Heart
disease
remains
a
significant
health
threat
due
to
its
high
mortality
rate
and
increasing
prevalence.
Early
prediction
using
basic
physical
markers
from
routine
exams
is
crucial
for
timely
diagnosis
intervention.
However,
manual
analysis
of
large
datasets
can
be
labor-intensive
error-prone.
Our
goal
rapidly
reliably
anticipate
cardiac
variety
body
signs.
This
research
presents
unique
model
heart
prediction.
We
provide
system
predicting
that
blends
the
deep
convolutional
neural
network
with
feature
selection
technique
based
on
LinearSVC.
integrated
method
selects
subset
characteristics
are
strongly
linked
disease.
feed
these
features
into
conventual
we
constructed.
Also
improve
speed
predictor
avoid
gradient
varnishing
or
explosion,
network's
hyperparameters
were
tuned
random
search
algorithm.
The
proposed
was
evaluated
UCI
MIT
datasets.
number
indicators,
such
as
accuracy,
recall,
precision,
F1
score.
results
demonstrate
our
attains
accuracy
rates
98.16%,
98.2%,
95.38%,
97.84%
in
dataset,
an
average
MCC
score
90%.
These
affirm
efficacy
reliability
predict
npj Systems Biology and Applications,
Год журнала:
2025,
Номер
11(1)
Опубликована: Апрель 18, 2025
Integrating
biological
data
with
in
silico
modeling
offers
the
transformative
potential
to
develop
virtual
human
models,
or
"digital
twins."
These
models
hold
immense
promise
for
deepening
our
understanding
of
diseases
and
uncovering
new
therapeutic
strategies.
This
approach
is
especially
valuable
lacking
reliable
models.
Here
we
review
current
modelling
efforts
lung
development,
highlighting
role
interdisciplinary
collaboration
key
advances
toward
a
digital
twin.
