Impact of gene-gene interactions in Progressive Supranuclear Palsy: new genetic perspectives in the Asian-Indian population
Journal of Neurogenetics,
Год журнала:
2025,
Номер
unknown, С. 1 - 7
Опубликована: Май 14, 2025
Genes
play
an
important
role
in
the
risk
of
Progressive
Supranuclear
Palsy
(PSP).
Some
major
genes
identified
for
PSP
include
MAPT,
STX6,
MOBP,
and
EIF2AK3
several
ethnic
groups.
However,
interactions
among
these
have
not
been
explored
PSP.
Therefore,
this
prospective
case-control
study
aimed
to
explore
impact
gene-gene
patients
with
(n
=
106)
healthy
subjects
109)
Indian
ethnicity.
Eight
single
nucleotide
polymorphisms
(SNPs)
MAPT
gene
(rs1467967,
rs242557,
rs3785883,
rs2471738,
rs8070723,
rs7521,
rs12185268,
rs62063857,
two
SNPs
STX6
(rs3747957
rs1411478),
one
SNP
each
from
MOBP
(rs1768208)
(rs7571971)
were
genotyped
by
TaqMan
Alleleic
Discrimination
Assay
all
participants.
Gene-gene
12
performed
using
multi-dimensionality
reduction
(MDR)
test.
The
combination
rs3785883),
along
(rs1411478)
(rs1768208),
appeared
be
best
five-locus
model
(p
<
0.001),
suggesting
strong
modulating
Strong
synergistic
observed
within
rs244557,
rs2471738),
between
(rs7521)
(rs1768208).
Additionally,
moderately
found
(i)
(rs1411478),
(ii)
(rs3785883)
genes.
findings
suggest
significant
amongst
This
implies
that
epistatic
might
constitute
mechanism
delineating
genetic
basis
Язык: Английский
Explainable machine learning on clinical features to predict and differentiate Alzheimer's progression by sex: Toward a clinician-tailored web interface
Journal of the Neurological Sciences,
Год журнала:
2024,
Номер
468, С. 123361 - 123361
Опубликована: Дек. 19, 2024
Alzheimer's
disease
(AD),
the
most
common
neurodegenerative
disorder
world-wide,
presents
sex-specific
differences
in
its
manifestation
and
progression,
necessitating
personalized
diagnostic
approaches.
Current
procedures
are
often
costly
invasive,
lacking
consideration
of
sex-based
differences.
This
study
introduces
an
explainable
machine
learning
(ML)
system
to
predict
differentiate
progression
AD
based
on
sex,
using
non-invasive,
easily
collectible
predictors
such
as
neuropsychological
test
scores
sociodemographic
data,
enabling
application
every
day
clinical
settings.
The
ML
model
uses
SHapley
Additive
explanations
(SHAP)
Local
Interpretable
Model-Agnostic
Explanations
(LIME)
provide
clear
insights
into
decision-making,
making
complex
outcomes
easier
interpret.
includes
a
user-friendly
graphical
interface
designed
collaboration
with
clinicians,
supporting
integration
medical
practice.
extends
cohort
include
healthy
Mild
Cognitive
Impairment
subjects,
aiming
support
early
diagnosis
pre-clinical
stages.
was
trained
large
dataset
2407
subjects
from
ADNI
open
dataset,
enhancing
robustness
applicability.
By
focusing
features
utilizing
longitudinal
aims
improve
prediction
accuracy
detection
AD,
ultimately
advancing
therapeutic
Key
findings
highlight
significance
Mini-Mental
State
Examination,
Rey
Auditory
Verbal
Learning
Test,
Logical
Memory
-
Delayed
Recall,
educational
attainment
disparities.
Despite
performance
metrics
precision,
recall,
weighted
F1-score
demonstrating
efficacy,
future
research
should
address
limitations
relying
single
dataset.
Язык: Английский
Sex-Specific Imaging Biomarkers for Parkinson’s Disease Diagnosis: A Machine Learning Analysis
Deleted Journal,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 10, 2024
This
study
aimed
to
identify
sex-specific
imaging
biomarkers
for
Parkinson's
disease
(PD)
based
on
multiple
MRI
morphological
features
by
using
machine
learning
methods.
Participants
were
categorized
into
female
and
male
subgroups,
various
structural
extracted.
An
ensemble
Lasso
(EnLasso)
method
was
employed
a
stable
optimal
feature
subset
each
sex-based
subgroup.
Eight
typical
classifiers
adopted
construct
classification
models
PD
HC,
respectively,
validate
whether
specific
sex
subgroups
could
bolster
the
precision
of
identification.
Finally,
statistical
analysis
correlation
tests
carried
out
significant
brain
region
potential
biomarkers.
The
best
model
(MLP)
subgroup
achieved
average
accuracy
92.83%
92.11%,
which
better
than
that
overall
samples
(86.88%)
incorporating
gender
factor
(87.52%).
In
addition,
most
discriminative
among
males
lh
6r
(FD),
but
females,
it
PreS
(GI).
findings
indicate
diagnosis
yields
significantly
higher
performance
compared
previous
included
all
participants.
Additionally,
exhibited
greater
number
changes
subgroup,
suggesting
differences
in
risk
markers.
underscore
importance
stratifying
data
offer
insights
variations
phenotypes,
aid
development
precise
personalized
diagnostic
approaches
early
stages
disease.
Язык: Английский