Elucidating the Functional Roles of Long Non-coding RNAs in Alzheimer's Disease
Опубликована: Июль 10, 2024
Alzheimer's
disease
(AD)
is
a
multifaceted
neurodegenerative
disorder
characterized
by
cognitive
decline
and
neuronal
loss,
representing
most
challenging
health
issue.
We
present
computational
analysis
of
transcriptomic
data
AD
tissues
vs.
healthy
controls,
focused
on
elucidation
functional
roles
played
long
non-coding
RNAs
(lncRNAs)
throughout
the
progression.
first
assembled
our
own
lncRNA
transcripts
from
raw
RNA-Seq
generated
527
samples
dorsolateral
prefrontal
cortex,
resulting
in
identification
31,574
novel
genes.
Based
co-expression
analyses
between
mRNAs
lncRNAs,
network
constructed.
Maximal
subnetworks
with
dense
connections
are
identified
as
clusters.
Pathway
enrichment
conducted
over
lncRNAs
each
cluster,
which
serve
basis
for
inference
involved
key
steps
an
development
model
that
we
have
previously
build
based
protein-encoding
Detailed
information
presented
about
activities
related
to
stress
response,
reprogrammed
metabolism,
cell-polarity,
development.
Our
also
revealed
discerning
power
distinguishing
stage
controls.
This
study
represents
its
kind.
Язык: Английский
Optimizing Model Performance and Interpretability: Application to Biological Data Classification
Genes,
Год журнала:
2025,
Номер
16(3), С. 297 - 297
Опубликована: Фев. 28, 2025
This
study
introduces
a
novel
framework
that
simultaneously
addresses
the
challenges
of
performance
accuracy
and
result
interpretability
in
transcriptomic-data-based
classification.
Background/objectives:
In
biological
data
classification,
it
is
challenging
to
achieve
both
high
at
same
time.
presents
address
The
goal
select
features,
models,
meta-voting
classifier
optimizes
classification
interpretability.
Methods:
consists
four-step
feature
selection
process:
(1)
identification
metabolic
pathways
whose
enzyme-gene
expressions
discriminate
samples
with
different
labels,
aiding
interpretability;
(2)
expression
variance
largely
captured
by
first
principal
component
gene
matrix;
(3)
minimal
sets
genes,
collective
discerning
power
covers
95%
pathway-based
power;
(4)
introduction
adversarial
identify
filter
genes
sensitive
such
samples.
Additionally,
are
used
optimal
model,
constructed
based
on
optimized
model
results.
Results:
applied
two
cancer
problems
showed
binary
prediction
was
comparable
full-gene
F1-score
differences
between
−5%
5%.
ternary
significantly
better,
ranging
from
−2%
12%,
while
also
maintaining
excellent
selected
genes.
Conclusions:
effectively
integrates
selection,
sample
handling,
optimization,
offering
valuable
tool
for
wide
range
problems.
Its
ability
balance
makes
highly
applicable
field
computational
biology.
Язык: Английский
A Map of Transcriptomic Signatures of Different Brain Areas in Alzheimer’s Disease
International Journal of Molecular Sciences,
Год журнала:
2024,
Номер
25(20), С. 11117 - 11117
Опубликована: Окт. 16, 2024
Alzheimer’s
disease
(AD)
is
a
neurodegenerative
disorder
that
progressively
involves
brain
regions
with
an
often-predictable
pattern.
Damage
to
the
appears
spread
and
worsen
time,
but
molecular
mechanisms
underlying
region-specific
distribution
of
AD
pathology
at
different
stages
are
still
under-investigated.
In
this
study,
whole-transcriptome
analysis
was
carried
out
on
samples
from
hippocampus
(HI),
temporal
parietal
cortices
(TC
PC,
respectively),
cingulate
cortex
(CG),
substantia
nigra
(SN)
six
subjects
definite
diagnosis
three
healthy
age-matched
controls
in
duplicate.
The
transcriptomic
results
showed
greater
number
differentially
expressed
genes
(DEGs)
TC
(1571)
CG
(1210)
smaller
DEGs
HI
(206),
PC
(109),
SN
(60).
Furthermore,
GSEA
difference
between
group
areas
affected
early
(HI
TC)
were
subsequently
involved
(PC,
CG,
SN).
Notably,
TC,
there
significant
downregulation
shared
primarily
synaptic
transmission,
while
SN,
protein
folding
trafficking.
course
could
follow
time-
severity-related
pattern
arises
misfolding,
as
observed
leads
impairment,
TC.
Therefore,
map
biological
processes
pathogenesis
may
be
traced.
This
aid
discovery
novel
targets
order
develop
effective
well-timed
therapeutic
approaches.
Язык: Английский
Construction of A Dataset for All Expressed Transcripts for Alzheimer’s Disease Research
Brain Sciences,
Год журнала:
2024,
Номер
14(12), С. 1180 - 1180
Опубликована: Ноя. 25, 2024
Accurate
identification
and
functional
annotation
of
splicing
isoforms
non-coding
RNAs
(lncRNAs),
alongside
full-length
protein-encoding
transcripts,
are
critical
for
understanding
gene
(mis)regulation
metabolic
reprogramming
in
Alzheimer’s
disease
(AD).
This
study
aims
to
provide
a
comprehensive
accurate
transcriptome
resource
improve
existing
AD
transcript
databases.
Background/Objectives:
Gene
mis-regulation
play
key
role
AD,
yet
databases
lack
lncRNAs.
generate
refined
dataset,
expanding
the
onset
progression.
Methods:
Publicly
available
RNA-seq
data
from
pre-AD
tissues
were
utilized.
Advanced
bioinformatics
tools
applied
assemble
annotate
including
lncRNAs,
with
an
emphasis
on
correcting
errors
enhancing
accuracy.
Results:
A
significantly
improved
dataset
was
generated,
which
includes
detailed
annotations
expands
scope
provides
new
insights
into
molecular
mechanisms
underlying
AD.
The
findings
demonstrate
that
captures
more
relevant
details
about
progression
compared
publicly
data.
Conclusions:
newly
developed
associated
analysis
offer
valuable
contribution
research,
providing
deeper
disease’s
mechanisms.
work
supports
future
research
regulation
serves
as
foundation
exploring
novel
therapeutic
targets.
Язык: Английский