Frontiers in Genetics,
Journal Year:
2024,
Volume and Issue:
15
Published: Feb. 20, 2024
Introduction:
As
the
evaluation
indices,
cancer
grading
and
subtyping
have
diverse
clinical,
pathological,
molecular
characteristics
with
prognostic
therapeutic
implications.
Although
researchers
begun
to
study
differentiation
subtype
prediction,
most
of
relevant
methods
are
based
on
traditional
machine
learning
rely
single
omics
data.
It
is
necessary
explore
a
deep
algorithm
that
integrates
multi-omics
data
achieve
classification
prediction
subtypes.
Methods:
This
paper
proposes
fusion
multi-view
graph
neural
network
(MVGNN)
for
predicting
classification.
The
model
framework
consists
convolutional
(GCN)
module
features
from
different
an
attention
integrating
Three
types
used.
For
each
type
data,
feature
selection
performed
using
such
as
chi-square
test
minimum
redundancy
maximum
relevance
(mRMR).
Weighted
patient
similarity
networks
constructed
selected
features,
GCN
trained
corresponding
networks.
Finally,
performs
final
prediction.
Results:
To
validate
predictive
performance
MVGNN
model,
we
conducted
experimental
comparisons
models
currently
popular
5-fold
cross-validation.
Additionally,
comparative
experiments
its
subtypes
two
three
Discussion:
proposed
it
well
in
multiple
Frontiers in Oncology,
Journal Year:
2023,
Volume and Issue:
12
Published: Jan. 4, 2023
Cancer
is
a
major
medical
problem
worldwide.
Due
to
its
high
heterogeneity,
the
use
of
same
drugs
or
surgical
methods
in
patients
with
tumor
may
have
different
curative
effects,
leading
need
for
more
accurate
treatment
tumors
and
personalized
treatments
patients.
The
precise
essential,
which
renders
obtaining
an
in-depth
understanding
changes
that
undergo
urgent,
including
their
genes,
proteins
cancer
cell
phenotypes,
order
develop
targeted
strategies
Artificial
intelligence
(AI)
based
on
big
data
can
extract
hidden
patterns,
important
information,
corresponding
knowledge
behind
enormous
amount
data.
For
example,
ML
deep
learning
subsets
AI
be
used
mine
deep-level
information
genomics,
transcriptomics,
proteomics,
radiomics,
digital
pathological
images,
other
data,
make
clinicians
synthetically
comprehensively
understand
tumors.
In
addition,
find
new
biomarkers
from
assist
screening,
detection,
diagnosis,
prognosis
prediction,
so
as
providing
best
individual
improving
clinical
outcomes.
Genome biology,
Journal Year:
2022,
Volume and Issue:
23(1)
Published: Aug. 9, 2022
A
fused
method
using
a
combination
of
multi-omics
data
enables
comprehensive
study
complex
biological
processes
and
highlights
the
interrelationship
relevant
biomolecules
their
functions.
Driven
by
high-throughput
sequencing
technologies,
several
promising
deep
learning
methods
have
been
proposed
for
fusing
generated
from
large
number
samples.
International Journal of Environmental Research and Public Health,
Journal Year:
2023,
Volume and Issue:
20(4), P. 3640 - 3640
Published: Feb. 18, 2023
The
overall
burden
of
cancer
is
rapidly
increasing
worldwide,
reflecting
not
only
population
growth
and
aging,
but
also
the
prevalence
spread
risk
factors.
Gastrointestinal
(GI)
cancers,
including
stomach,
liver,
esophageal,
pancreatic,
colorectal
represent
more
than
a
quarter
all
cancers.
While
smoking
alcohol
use
are
factors
most
commonly
associated
with
development,
growing
consensus
includes
dietary
habits
as
relevant
for
GI
Current
evidence
suggests
that
socioeconomic
development
results
in
several
lifestyle
modifications,
shifts
from
local
traditional
diets
to
less-healthy
Western
diets.
Moreover,
recent
data
indicate
increased
production
consumption
processed
foods
underlies
current
pandemics
obesity
related
metabolic
disorders,
which
directly
or
indirectly
emergence
various
chronic
noncommunicable
conditions
However,
environmental
changes
restricted
patterns,
unhealthy
behavioral
features
should
be
analyzed
holistic
view
lifestyle.
In
this
review,
we
discussed
epidemiological
aspects,
gut
dysbiosis,
cellular
molecular
characteristics
cancers
explored
impact
behaviors,
diet,
physical
activity
on
developing
context
progressive
societal
changes.
Frontiers in Artificial Intelligence,
Journal Year:
2024,
Volume and Issue:
7
Published: July 25, 2024
Cancer
research
encompasses
data
across
various
scales,
modalities,
and
resolutions,
from
screening
diagnostic
imaging
to
digitized
histopathology
slides
types
of
molecular
clinical
records.
The
integration
these
diverse
for
personalized
cancer
care
predictive
modeling
holds
the
promise
enhancing
accuracy
reliability
screening,
diagnosis,
treatment.
Traditional
analytical
methods,
which
often
focus
on
isolated
or
unimodal
information,
fall
short
capturing
complex
heterogeneous
nature
data.
advent
deep
neural
networks
has
spurred
development
sophisticated
multimodal
fusion
techniques
capable
extracting
synthesizing
information
disparate
sources.
Among
these,
Graph
Neural
Networks
(GNNs)
Transformers
have
emerged
as
powerful
tools
learning,
demonstrating
significant
success.
This
review
presents
foundational
principles
learning
including
oncology
taxonomy
strategies.
We
delve
into
recent
advancements
in
GNNs
oncology,
spotlighting
key
studies
their
pivotal
findings.
discuss
unique
challenges
such
heterogeneity
complexities,
alongside
opportunities
it
a
more
nuanced
comprehensive
understanding
cancer.
Finally,
we
present
some
latest
pan-cancer
By
surveying
landscape
our
goal
is
underline
transformative
potential
Transformers.
Through
technological
methodological
innovations
presented
this
review,
aim
chart
course
future
promising
field.
may
be
first
that
highlights
current
state
applications
using
transformers,
sources,
sets
stage
evolution,
encouraging
further
exploration
care.
Briefings in Bioinformatics,
Journal Year:
2023,
Volume and Issue:
24(2)
Published: Jan. 25, 2023
Abstract
Due
to
the
high
heterogeneity
and
complexity
of
cancers,
patients
with
different
cancer
subtypes
often
have
distinct
groups
genomic
clinical
characteristics.
Therefore,
discovery
identification
are
crucial
diagnosis,
prognosis
treatment.
Recent
technological
advances
accelerated
increasing
availability
multi-omics
data
for
subtyping.
To
take
advantage
complementary
information
from
data,
it
is
necessary
develop
computational
models
that
can
represent
integrate
layers
into
a
single
framework.
Here,
we
propose
decoupled
contrastive
clustering
method
(Subtype-DCC)
based
on
integration
identify
subtypes.
The
idea
learning
introduced
deep
neural
networks
learn
clustering-friendly
representations.
Experimental
results
demonstrate
superior
performance
proposed
Subtype-DCC
model
in
identifying
over
currently
available
state-of-the-art
methods.
strength
also
supported
by
survival
analysis.
Briefings in Functional Genomics,
Journal Year:
2024,
Volume and Issue:
23(5), P. 549 - 560
Published: April 10, 2024
Multi-omics
data
play
a
crucial
role
in
precision
medicine,
mainly
to
understand
the
diverse
biological
interaction
between
different
omics.
Machine
learning
approaches
have
been
extensively
employed
this
context
over
years.
This
review
aims
comprehensively
summarize
and
categorize
these
advancements,
focusing
on
integration
of
multi-omics
data,
which
includes
genomics,
transcriptomics,
proteomics
metabolomics,
alongside
clinical
data.
We
discuss
various
machine
techniques
computational
methodologies
used
for
integrating
distinct
omics
datasets
provide
valuable
insights
into
their
application.
The
emphasizes
both
challenges
opportunities
present
integration,
medicine
patient
stratification,
offering
practical
recommendations
method
selection
scenarios.
Recent
advances
deep
network-based
are
also
explored,
highlighting
potential
harmonize
information
layers.
Additionally,
we
roadmap
oncology,
outlining
advantages,
implementation
difficulties.
Hence
offers
thorough
overview
current
literature,
providing
researchers
with
particularly
oncology.
Contact:
[email protected].
Artificial Intelligence Chemistry,
Journal Year:
2024,
Volume and Issue:
2(2), P. 100077 - 100077
Published: Aug. 31, 2024
Molecular
similarity
pervades
much
of
our
understanding
and
rationalization
chemistry.
This
has
become
particularly
evident
in
the
current
data-intensive
era
chemical
research,
with
measures
serving
as
backbone
many
Machine
Learning
(ML)
supervised
unsupervised
procedures.
Here,
we
present
a
discussion
on
role
molecular
drug
design,
space
exploration,
"art"
generation,
representations,
more.
We
also
discuss
more
recent
topics
similarity,
like
ability
to
efficiently
compare
large
libraries.
Deleted Journal,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 6, 2025
Abstract
Artificial
intelligence
(AI)
is
rapidly
advancing,
yet
its
applications
in
radiology
remain
relatively
nascent.
From
a
spatiotemporal
perspective,
this
review
examines
the
forces
driving
AI
development
and
integration
with
medicine
radiology,
particular
focus
on
advancements
addressing
major
diseases
that
significantly
threaten
human
health.
Temporally,
advent
of
foundational
model
architectures,
combined
underlying
drivers
development,
accelerating
progress
interventions
their
practical
applications.
Spatially,
discussion
explores
potential
evolving
methodologies
to
strengthen
interdisciplinary
within
medicine,
emphasizing
four
critical
points
imaging
process,
as
well
application
disease
management,
including
emergence
commercial
products.
Additionally,
current
utilization
deep
learning
reviewed,
future
through
multimodal
foundation
models
Generative
Pre‐trained
Transformer
are
anticipated.