Clinical Breast Cancer,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 1, 2025
Older
adult
breast
cancer
(OABC)
patients
(≥
65
years)
frequently
experience
poorer
prognoses
compared
to
younger
adults,
attributed
complex
tumor
biology
and
age-related
factors.
The
present
study
employs
a
multiomics
approach
combined
with
machine
learning
develop
novel
prognostic
model
for
OABC,
focus
on
the
hypoxic
immune
characteristics
of
microenvironment.
Genetic
molecular
data
from
503
OABC
589
(YABC)
were
analyzed
using
Cancer
Genome
Atlas
(TCGA)
database.
An
ensemble
machine-learning
was
developed,
integrating
data-including
mRNA,
miRNA,
lncRNA,
copy
number
variations
(CNVs),
single
nucleotide
variants
(SNVs)-along
clinicopathological
features,
predict
survival
outcomes.
trained
300
samples
validated
203
samples.
achieved
predictive
accuracy
69.5%
outcomes
in
patients.
Distinct
hypoxia-related
gene
expression
patterns
reduced
cell
infiltration
observed
YABC.
Hypoxia
significantly
associated
disease-free
(DFS)
(P
=
.037),
but
not
YABC
.38).
multiomics-based
developed
showed
clinical
potential,
findings
highlight
critical
role
hypoxia
microenvironment
prognosis
OABC.
Further
research
is
warranted
validate
this
larger
cohorts
explore
its
potential
application
guiding
personalized
treatment
strategies
Biology,
Год журнала:
2024,
Номер
13(11), С. 848 - 848
Опубликована: Окт. 22, 2024
With
the
advent
of
high-throughput
technologies,
field
omics
has
made
significant
strides
in
characterizing
biological
systems
at
various
levels
complexity.
Transcriptomics,
proteomics,
and
metabolomics
are
three
most
widely
used
each
providing
unique
insights
into
different
layers
a
system.
However,
analyzing
data
set
separately
may
not
provide
comprehensive
understanding
subject
under
study.
Therefore,
integrating
multi-omics
become
increasingly
important
bioinformatics
research.
In
this
article,
we
review
strategies
for
transcriptomics,
data,
including
co-expression
analysis,
metabolite-gene
networks,
constraint-based
models,
pathway
enrichment
interactome
analysis.
We
discuss
combined
integration
approaches,
correlation-based
strategies,
machine
learning
techniques
that
utilize
one
or
more
types
data.
By
presenting
these
methods,
aim
to
researchers
with
better
how
integrate
gain
view
system,
facilitating
identification
complex
patterns
interactions
might
be
missed
by
single-omics
analyses.
Cancer Discovery,
Год журнала:
2022,
Номер
12(6), С. 1423 - 1427
Опубликована: Июнь 2, 2022
Summary:
Artificial
intelligence
(AI)
and
machine
learning
(ML)
technologies
have
not
only
tremendous
potential
to
augment
clinical
decision-making
enhance
quality
care
precision
medicine
efforts,
but
also
the
worsen
existing
health
disparities
without
a
thoughtful,
transparent,
inclusive
approach
that
includes
addressing
bias
in
their
design
implementation
along
cancer
discovery
continuum.
We
discuss
applications
of
AI/ML
tools
provide
recommendations
for
mitigating
with
AI
ML
while
promoting
equity.
Computational and Structural Biotechnology Journal,
Год журнала:
2023,
Номер
21, С. 1372 - 1382
Опубликована: Янв. 1, 2023
Cancer
progression
is
linked
to
gene-environment
interactions
that
alter
cellular
homeostasis.
The
use
of
biomarkers
as
early
indicators
disease
manifestation
and
can
substantially
improve
diagnosis
treatment.
Large
omics
datasets
generated
by
high-throughput
profiling
technologies,
such
microarrays,
RNA
sequencing,
whole-genome
shotgun
nuclear
magnetic
resonance,
mass
spectrometry,
have
enabled
data-driven
biomarker
discoveries.
identification
differentially
expressed
traits
molecular
markers
has
traditionally
relied
on
statistical
techniques
are
often
limited
linear
parametric
modeling.
heterogeneity,
epigenetic
changes,
high
degree
polymorphism
observed
in
oncogenes
demand
biomarker-assisted
personalized
medication
schemes.
Deep
learning
(DL),
a
major
subunit
machine
(ML),
been
increasingly
utilized
recent
years
investigate
various
diseases.
combination
ML/DL
approaches
for
performance
optimization
across
multi-omics
produces
robust
ensemble-learning
prediction
models,
which
becoming
useful
precision
medicine.
This
review
focuses
the
development
methods
provide
integrative
solutions
discovering
cancer-related
biomarkers,
their
utilization
Frontiers in Oncology,
Год журнала:
2023,
Номер
13
Опубликована: Май 17, 2023
Breast
cancer
is
a
highly
heterogeneous
disease,
at
both
inter-
and
intra-tumor
levels,
this
heterogeneity
crucial
determinant
of
malignant
progression
response
to
treatments.
In
addition
genetic
diversity
plasticity
cells,
the
tumor
microenvironment
contributes
shaping
physical
biological
surroundings
tumor.
The
activity
certain
types
immune,
endothelial
or
mesenchymal
cells
in
can
change
effectiveness
therapies
via
plethora
different
mechanisms.
Therefore,
deciphering
interactions
between
distinct
cell
types,
their
spatial
organization
specific
contribution
growth
drug
sensitivity
still
major
challenge.
Dissecting
currently
an
urgent
need
better
define
breast
biology
develop
therapeutic
strategies
targeting
as
helpful
tools
for
combined
personalized
treatment.
review,
we
analyze
mechanisms
by
which
affects
characteristics
that
ultimately
result
resistance,
outline
state
art
preclinical
models
emerging
technologies
will
be
instrumental
unraveling
impact
on
resistance
therapies.
Biology,
Год журнала:
2023,
Номер
12(10), С. 1298 - 1298
Опубликована: Сен. 30, 2023
This
review
discusses
the
transformative
potential
of
integrating
multi-omics
data
and
artificial
intelligence
(AI)
in
advancing
horticultural
research,
specifically
plant
phenotyping.
The
traditional
methods
phenotyping,
while
valuable,
are
limited
their
ability
to
capture
complexity
biology.
advent
(meta-)genomics,
(meta-)transcriptomics,
proteomics,
metabolomics
has
provided
an
opportunity
for
a
more
comprehensive
analysis.
AI
machine
learning
(ML)
techniques
can
effectively
handle
volume
data,
providing
meaningful
interpretations
predictions.
Reflecting
multidisciplinary
nature
this
area
review,
readers
will
find
collection
state-of-the-art
solutions
that
key
integration
phenotyping
experiments
horticulture,
including
experimental
design
considerations
with
several
technical
non-technical
challenges,
which
discussed
along
solutions.
future
prospects
include
precision
predictive
breeding,
improved
disease
stress
response
management,
sustainable
crop
exploration
biodiversity.
holds
immense
promise
revolutionizing
research
applications,
heralding
new
era
Journal of Biological Engineering,
Год журнала:
2023,
Номер
17(1)
Опубликована: Апрель 17, 2023
Abstract
Background
Early
diagnosis
of
Pancreatic
Ductal
Adenocarcinoma
(PDAC)
is
the
main
key
to
surviving
cancer
patients.
Urine
proteomic
biomarkers
which
are
creatinine,
LYVE1,
REG1B,
and
TFF1
present
a
promising
non-invasive
inexpensive
diagnostic
method
PDAC.
Recent
utilization
both
microfluidics
technology
artificial
intelligence
techniques
enables
accurate
detection
analysis
these
biomarkers.
This
paper
proposes
new
deep-learning
model
identify
urine
for
automated
pancreatic
cancers.
The
proposed
composed
one-dimensional
convolutional
neural
networks
(1D-CNNs)
long
short-term
memory
(LSTM).
It
can
categorize
patients
into
healthy
pancreas,
benign
hepatobiliary
disease,
PDAC
cases
automatically.
Results
Experiments
evaluations
have
been
successfully
done
on
public
dataset
590
samples
three
classes,
183
pancreas
samples,
208
disease
199
samples.
results
demonstrated
that
our
1-D
CNN
+
LSTM
achieved
best
accuracy
score
97%
area
under
curve
(AUC)
98%
versus
state-of-the-art
models
diagnose
cancers
using
Conclusion
A
efficient
1D
CNN-LSTM
has
developed
early
four
TFF1.
showed
superior
performance
other
machine
learning
classifiers
in
previous
studies.
prospect
this
study
laboratory
realization
deep
classifier
urinary
biomarker
panels
assisting
procedures
Cancer Informatics,
Год журнала:
2022,
Номер
21, С. 117693512211242 - 117693512211242
Опубликована: Янв. 1, 2022
Multi-omics
data
integration
facilitates
collecting
richer
understanding
and
perceptions
than
separate
omics
data.
Various
promising
integrative
approaches
have
been
utilized
to
analyze
multi-omics
for
biomedical
applications,
including
disease
prediction
subtypes,
biomarker
prediction,
others.In
this
paper,
we
introduce
a
method
that
is
constructed
using
the
combination
of
gene
similarity
network
(GSN)
based
on
uniform
manifold
approximation
projection
(UMAP)
convolutional
neural
networks
(CNNs).
The
utilizes
UMAP
embed
expression,
DNA
methylation,
copy
number
alteration
(CNA)
lower
dimension
creating
two-dimensional
RGB
images.
Gene
expression
used
as
reference
construct
GSN
then
integrate
other
with
better
prediction.
We
CNNs
predict
Gleason
score
levels
prostate
cancer
patients
tumor
stage
in
breast
patients.The
model
proposed
near
perfection
accuracy
above
99%
all
performance
measurements
at
same
level.
outperformed
state-of-art
iSOM-GSN
constructs
map
self-organizing
map.The
results
show
an
embedding
technique
can
maps
into
SOM.
also
be
applied
build
types
cancer.
Genes,
Год журнала:
2023,
Номер
14(4), С. 801 - 801
Опубликована: Март 26, 2023
Precision
and
organization
govern
the
cell
cycle,
ensuring
normal
proliferation.
However,
some
cells
may
undergo
abnormal
divisions
(neosis)
or
variations
of
mitotic
cycles
(endopolyploidy).
Consequently,
formation
polyploid
giant
cancer
(PGCCs),
critical
for
tumor
survival,
resistance,
immortalization,
can
occur.
Newly
formed
end
up
accessing
numerous
multicellular
unicellular
programs
that
enable
metastasis,
drug
recurrence,
self-renewal
diverse
clone
formation.
An
integrative
literature
review
was
carried
out,
searching
articles
in
several
sites,
including:
PUBMED,
NCBI-PMC,
Google
Academic,
published
English,
indexed
referenced
databases
without
a
publication
time
filter,
but
prioritizing
from
last
3
years,
to
answer
following
questions:
(i)
"What
is
current
knowledge
about
polyploidy
tumors?";
(ii)
are
applications
computational
studies
understanding
polyploidy?";
(iii)
"How
do
PGCCs
contribute
tumorigenesis?"
Heliyon,
Год журнала:
2024,
Номер
10(3), С. e25369 - e25369
Опубликована: Фев. 1, 2024
In
recent
years,
scientific
data
on
cancer
has
expanded,
providing
potential
for
a
better
understanding
of
malignancies
and
improved
tailored
care.
Advances
in
Artificial
Intelligence
(AI)
processing
power
algorithmic
development
position
Machine
Learning
(ML)
Deep
(DL)
as
crucial
players
predicting
Leukemia,
blood
cancer,
using
integrated
multi-omics
technology.
However,
realizing
these
goals
demands
novel
approaches
to
harness
this
deluge.
This
study
introduces
Leukemia
diagnosis
approach,
analyzing
accuracy
ML
DL
algorithms.
techniques,
including
Random
Forest
(RF),
Naive
Bayes
(NB),
Decision
Tree
(DT),
Logistic
Regression
(LR),
Gradient
Boosting
(GB),
methods
such
Recurrent
Neural
Networks
(RNN)
Feedforward
(FNN)
are
compared.
GB
achieved
97
%
ML,
while
RNN
outperformed
by
achieving
98
DL.
approach
filters
unclassified
effectively,
demonstrating
the
significance
leukemia
prediction.
The
testing
validation
was
based
17
different
features
patient
age,
sex,
mutation
type,
treatment
methods,
chromosomes,
others.
Our
compares
techniques
chooses
best
technique
that
gives
optimum
results.
emphasizes
implications
high-throughput
technology
healthcare,
offering
Frontiers in Cell and Developmental Biology,
Год журнала:
2024,
Номер
12
Опубликована: Июль 2, 2024
The
connection
and
causality
between
cancer
neurodevelopmental
disorders
have
been
puzzling.
How
can
the
same
cellular
pathways,
proteins,
mutations
lead
to
pathologies
with
vastly
different
clinical
presentations?
And
why
do
individuals
disorders,
such
as
autism
schizophrenia,
face
higher
chances
of
emerging
throughout
their
lifetime?
Our
broad
review
emphasizes
multi-scale
aspect
this
type
reasoning.
As
these
examples
demonstrate,
rather
than
focusing
on
a
specific
organ
system
or
disease,
we
aim
at
new
understanding
that
be
gained.
Within
framework,
our
calls
attention
computational
strategies
which
powerful
in
discovering
connections,
causalities,
predicting
outcomes,
are
vital
for
drug
discovery.
Thus,
centering
features,
draw
rapidly
increasing
data
molecular
level,
including
mutations,
isoforms,
three-dimensional
structures,
expression
levels
respective
disease-associated
genes.
Their
integrated
analysis,
together
chromatin
states,
delineate
how,
despite
being
connected,
differ,
how
symptoms.
Here,
seek
uncover
cancer,
pediatric
tumors,
tantalizing
questions
raises.