bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Дек. 4, 2023
Abstract
Epigenetic
aging
clocks
have
been
widely
used
to
validate
rejuvenation
effects
during
cellular
reprogramming.
However,
these
predictions
are
unverifiable
because
the
true
biological
age
of
reprogrammed
cells
remains
unknown.
We
present
an
analytical
framework
consider
from
uncertainty
perspective.
Our
analysis
reveals
that
DNA
methylation
profiles
across
reprogramming
poorly
represented
in
data
train
clock
models,
thus
introducing
high
epistemic
estimations.
Moreover,
different
published
inconsistent,
with
some
even
suggesting
zero
or
negative
rejuvenation.
While
not
questioning
possibility
reversal,
we
show
challenges
reliability
observed
vitro
before
pluripotency
and
throughout
embryogenesis.
Conversely,
our
method
a
significant
increase
after
vivo
recommend
including
estimation
future
models
avoid
risk
misinterpreting
results
prediction.
PLoS Computational Biology,
Год журнала:
2025,
Номер
21(1), С. e1012739 - e1012739
Опубликована: Янв. 10, 2025
Transfer
learning
aims
to
integrate
useful
information
from
multi-source
datasets
improve
the
performance
of
target
data.
This
can
be
effectively
applied
in
genomics
when
we
learn
gene
associations
a
tissue,
and
data
other
tissues
integrated.
However,
heavy-tail
distribution
outliers
are
common
data,
which
poses
challenges
effectiveness
current
transfer
approaches.
In
this
paper,
study
problem
under
high-dimensional
linear
models
with
t-distributed
error
(Trans-PtLR),
estimation
prediction
by
borrowing
source
offering
robustness
accommodate
complex
heavy
tails
outliers.
oracle
case
known
transferable
datasets,
algorithm
based
on
penalized
maximum
likelihood
expectation-maximization
is
established.
To
avoid
including
non-informative
sources,
propose
select
sources
cross-validation.
Extensive
simulation
experiments
as
well
an
application
demonstrate
that
Trans-PtLR
demonstrates
better
exist
compared
for
regression
model
normal
distribution.
Data
integration,
Variable
selection,
T
distribution,
Expectation
maximization
algorithm,
Genotype-Tissue
Expression,
Cross
validation.
Abstract
CD8
+
T
cell
activation
via
immune
checkpoint
blockade
(ICB)
is
successful
in
microsatellite
instable
(MSI)
colorectal
cancer
(CRC)
patients.
By
comparison,
the
success
of
immunotherapy
against
stable
(MSS)
CRC
limited.
Little
known
about
most
critical
features
cells
that
together
determine
diverse
landscapes
and
contrasting
ICB
responses.
Hence,
we
pursued
a
deep
single
mapping
on
transcriptomic
receptor
(TCR)
repertoire
levels
patient
cohort,
with
additional
surface
proteome
validation.
This
revealed
dynamics
are
underscored
by
complex
interactions
between
interferon-γ
signaling,
tumor
reactivity,
TCR
repertoire,
(predicted)
antigen-specificities,
environmental
cues
like
gut
microbiome
or
colon
tissue-specific
‘self-like’
features.
MSI
showed
tumor-specific
reminiscent
canonical
‘T
hot’
tumors,
whereas
MSS
exhibited
unspecific
bystander-like
was
accompanied
inflammation
‘pseudo-T
tumors.
Consequently,
overlapping
phenotypic
differed
dramatically
their
antigen-specificities.
Given
high
discriminating
potential
for
features/specificities,
used
tumor-reactive
signaling
modules
to
build
bulk
transcriptome
classification
“Immune
Subtype
Classification”
(ISC)
successfully
distinguished
various
tumoral
prognostic
value
predicted
responses
Thus,
deliver
unique
map
drives
novel
landscape
classification,
relevance
decision-making.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Апрель 24, 2024
Abstract
Metaproteomics
analyzes
the
functional
dynamics
of
microbial
communities
by
identifying
peptides
and
mapping
them
to
most
likely
proteins
taxa.
The
challenge
in
this
field
lies
seamlessly
integrating
taxonomic
annotations
accurately
represent
contributions
individual
taxa
diversity.
We
introduce
MetaX,
a
comprehensive
tool
for
analyzing
taxa-function
relationships
metaproteomics
their
lowest
common
ancestors
assigning
functions
based
on
proportional
thresholds,
ensuring
accurate
peptide-level
mappings.
Importantly,
MetaX
introduces
Operational
Taxa-Functions
(OTF),
new
conceptual
unit
exploring
roles
interactions
within
ecosystems.
Additionally,
extends
traditional
classification
adding
genome
level
below
species
level,
enhancing
accuracy
function
attribution
specific
genomes.
demonstrated
reanalyzing
metaproteomic
data
from
gut
microbiomes
exposed
various
sweeteners,
achieving
results
similar
protein
analysis.
Furthermore,
using
peptide-centric
approach
OTF,
we
observed
that
Parabacteroides
distasonis
significantly
responds
certain
highlighting
its
role
modifying
metabolic
functions.
With
intuitive,
user-friendly
interface,
facilitates
detailed
study
complex
between
metaproteomics.
It
enhances
our
understanding
ecosystems
health.
Abstract
Epigenetic
aging
clocks
have
been
widely
used
to
validate
rejuvenation
effects
during
cellular
reprogramming.
However,
these
predictions
are
unverifiable
because
the
true
biological
age
of
reprogrammed
cells
remains
unknown.
We
present
an
analytical
framework
consider
from
uncertainty
perspective.
Our
analysis
reveals
that
DNA
methylation
profiles
across
reprogramming
poorly
represented
in
data
train
clock
models,
thus
introducing
high
epistemic
estimations.
Moreover,
different
published
inconsistent,
with
some
even
suggesting
zero
or
negative
rejuvenation.
While
not
questioning
possibility
reversal,
we
show
challenges
reliability
observed
vitro
before
pluripotency
and
throughout
embryogenesis.
Conversely,
our
method
a
significant
increase
after
vivo
recommend
including
estimation
future
models
avoid
risk
misinterpreting
results
prediction.
Cellular
senescence,
a
hallmark
of
aging,
reveals
context-dependent
phenotypes
across
multiple
biological
length
scales.
Despite
its
mechanistic
importance,
identifying
and
characterizing
senescence
cell
populations
is
challenging.
Using
primary
dermal
fibroblasts,
we
combined
single-cell
imaging,
machine
learning,
several
induced
conditions,
protein
biomarkers
to
define
functional
subtypes.
Single-cell
morphology
analysis
revealed
11
distinct
clusters.
Among
these,
identified
three
as
bona
fide
subtypes
(C7,
C10,
C11),
with
C10
exhibiting
the
strongest
age
dependence
within
an
aging
cohort.
In
addition,
observed
that
donor’s
burden
subtype
composition
were
indicative
susceptibility
doxorubicin-induced
senescence.
Functional
subtype-dependent
responses
senotherapies,
C7
being
most
responsive
combination
dasatinib
quercetin.
Our
framework,
SenSCOUT,
enables
robust
identification
classification
subtypes,
offering
applications
in
next-generation
senotherapy
screens,
potential
toward
explaining
heterogeneous
based
on
presence
European journal of medical research,
Год журнала:
2023,
Номер
28(1)
Опубликована: Ноя. 16, 2023
Abstract
Background
Membranous
nephropathy
(MN)
is
a
chronic
glomerular
disease
that
leads
to
nephrotic
syndrome
in
adults.
The
aim
of
this
study
was
identify
novel
biomarkers
and
immune-related
mechanisms
the
progression
MN
through
an
integrated
bioinformatics
approach.
Methods
microarray
data
were
downloaded
from
Gene
Expression
Omnibus
(GEO)
database.
differentially
expressed
genes
(DEGs)
between
normal
samples
identified
analyzed
by
Ontology
analysis,
Kyoto
Encyclopedia
Genes
Genomes
analysis
Set
Enrichment
Analysis
(GSEA)
enrichment.
Hub
hub
screened
weighted
gene
co-expression
network
(WGCNA)
least
absolute
shrinkage
selection
operator
(LASSO)
algorithm.
receiver
operating
characteristic
(ROC)
curves
evaluated
diagnostic
value
genes.
single-sample
GSEA
infiltration
degree
several
immune
cells
their
correlation
with
Results
We
total
574
DEGs.
enrichment
showed
metabolic
functions
pathways
significantly
enriched.
Four
modules
obtained
using
WGCNA.
candidate
signature
intersected
DEGs
then
subjected
LASSO
obtaining
6
ROC
indicated
associated
high
value.
CD4
+
T
cells,
CD8
B
infiltrated
correlated
Conclusions
six
(
ZYX
,
CD151
N4BP2L2-IT2
TAPBP
FRAS1
SCARNA9
)
as
for
MN,
providing
potential
targets
diagnosis
treatment.
Briefings in Bioinformatics,
Год журнала:
2024,
Номер
25(6)
Опубликована: Сен. 23, 2024
Abstract
Batch
effects
introduce
significant
variability
into
high-dimensional
data,
complicating
accurate
analysis
and
leading
to
potentially
misleading
conclusions
if
not
adequately
addressed.
Despite
technological
algorithmic
advancements
in
biomedical
research,
effectively
managing
batch
remains
a
complex
challenge
requiring
comprehensive
considerations.
This
paper
underscores
the
necessity
of
flexible
holistic
approach
for
selecting
effect
correction
algorithms
(BECAs),
advocating
proper
BECA
evaluations
consideration
artificial
intelligence–based
strategies.
We
also
discuss
key
challenges
correction,
including
importance
uncovering
hidden
factors
understanding
impact
design
imbalance,
missing
values,
aggressive
correction.
Our
aim
is
provide
researchers
with
robust
framework
effective
management
enhancing
reliability
data
analyses.
