Multicomponent Mendelian randomization and machine learning studies of potential drug targets for neurodegenerative diseases
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 18, 2025
Abstract
Neurodegenerative
diseases
(NDDs)
remain
a
global
health
challenge.
Alzheimer's
disease
(AD)
and
Parkinson's
(PD)
are
the
main
types
of
NDDs
worldwide,
Mendelian
Randomization
(MR)
analysis
across
multi-omics
entire
genome
offers
novel
strategies
for
identifying
potential
drug
targets.
This
study
used
MR
summary-based
MR(SMR)
to
explore
causal
relationship
between
genes
NDDs.
Colocalization
machine
learning
further
validated
reinforced
findings.
The
pharmacological
activity
candidate
targets
was
confirmed
via
molecular
docking
Molecular
dynamics.
revealed
14
that
were
closely
associated
with
both
Specifically,
IQCE(AD),
HDHD2(AD),
COMMD10(AD),
ALPP
(AD),
FXYD6
STK3
(PD),
LHFPL2
ENPP4
identified
as
risk
factors
(OR
>
1),
whereas
HEXIM2
TSC22D4
CHRNB1
BAG4
SLC25A1
IL15
protective
<
1).
results
strong
binding
activities
PREDNISOLONE(ALPP
=
-7.6
kcal/mol),
PANCURONIUM
BROMIDE(CHRNB1
-8
CHEMBL379975(STK3
=-10.7
kcal/mol)
SIROLIMUS(IL15
-9
kcal/mol).
dynamics
simulations
stable
IL15-Sirolimus,
ALPP-Prednisolone,
STK3-CHEMBL379975,
CHRNB1-Rocuronium
bromide
complexes.
promising
therapeutic
NDDs,
providing
new
insights
targeted
therapies
clinical
Our
provide
evidence
future
studies
aimed
at
developing
appropriate
interventions.
Язык: Английский
An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Май 20, 2025
Язык: Английский