Non-communicable
diseases
(NCDs)
such
as
cardiovascular
diseases,
chronic
respiratory
cancers,
diabetes,
and
mental
health
disorders
pose
a
significant
global
challenge,
accounting
for
the
majority
of
fatalities
disability-adjusted
life
years
worldwide.
These
arise
from
complex
interactions
between
genetic,
behavioral,
environmental
factors,
necessitating
thorough
understanding
these
dynamics
to
identify
effective
diagnostic
strategies
interventions.
Although
recent
advances
in
multi-omics
technologies
have
greatly
enhanced
our
ability
explore
interactions,
several
challenges
remain.
include
inherent
complexity
heterogeneity
multi-omic
datasets,
limitations
analytical
approaches,
severe
underrepresentation
non-European
genetic
ancestries
most
omics
which
restricts
generalizability
findings
exacerbates
disparities.
This
scoping
review
evaluates
landscape
data
related
NCDs
2000
2024,
focusing
on
advancements
integration,
translational
applications,
equity
considerations.
We
highlight
need
standardized
protocols,
harmonized
data-sharing
policies,
advanced
approaches
artificial
intelligence/machine
learning
integrate
study
gene-environment
interactions.
also
opportunities
translating
insights
(GxE)
research
into
precision
medicine
strategies.
underscore
potential
advancing
enhancing
patient
outcomes
across
diverse
underserved
populations,
emphasizing
fairness-centered
strategic
investments
build
local
capacities
underrepresented
populations
regions.
iScience,
Год журнала:
2022,
Номер
25(2), С. 103798 - 103798
Опубликована: Янв. 22, 2022
Multi-omics
data
analysis
is
an
important
aspect
of
cancer
molecular
biology
studies
and
has
led
to
ground-breaking
discoveries.
Many
efforts
have
been
made
develop
machine
learning
methods
that
automatically
integrate
omics
data.
Here,
we
review
tools
categorized
as
either
general-purpose
or
task-specific,
covering
both
supervised
unsupervised
for
integrative
multi-omics
We
benchmark
the
performance
five
approaches
using
from
Cancer
Cell
Line
Encyclopedia,
reporting
accuracy
on
type
classification
mean
absolute
error
drug
response
prediction,
evaluating
runtime
efficiency.
This
provides
recommendations
researchers
regarding
suitable
method
selection
their
specific
applications.
It
should
also
promote
development
novel
methodologies
integration,
which
will
be
essential
discovery,
clinical
trial
design,
personalized
treatments.
Molecular Plant,
Год журнала:
2022,
Номер
15(11), С. 1664 - 1695
Опубликована: Сен. 7, 2022
The
first
paradigm
of
plant
breeding
involves
direct
selection-based
phenotypic
observation,
followed
by
predictive
using
statistical
models
for
quantitative
traits
constructed
based
on
genetic
experimental
design
and,
more
recently,
incorporation
molecular
marker
genotypes.
However,
performance
or
phenotype
(P)
is
determined
the
combined
effects
genotype
(G),
envirotype
(E),
and
environment
interaction
(GEI).
Phenotypes
can
be
predicted
precisely
training
a
model
data
collected
from
multiple
sources,
including
spatiotemporal
omics
(genomics,
phenomics,
enviromics
across
time
space).
Integration
3D
information
profiles
(G-P-E),
each
with
multidimensionality,
provides
both
tremendous
opportunities
great
challenges.
Here,
we
review
innovative
technologies
breeding.
We
then
evaluate
multidimensional
that
integrated
strategy,
particularly
envirotypic
data,
which
have
largely
been
neglected
in
collection
are
nearly
untouched
construction.
propose
smart
scheme,
genomic-enviromic
prediction
(iGEP),
as
an
extension
genomic
prediction,
multiomics
information,
big
technology,
artificial
intelligence
(mainly
focused
machine
deep
learning).
discuss
how
to
implement
iGEP,
models,
environmental
indices,
factorial
structure
cross-species
prediction.
A
strategy
proposed
prediction-based
crop
redesign
at
macro
(individual,
population,
species)
micro
(gene,
metabolism,
network)
scales.
Finally,
provide
perspectives
translating
into
gain
through
integrative
platforms
open-source
initiatives.
call
coordinated
efforts
institutional
partnerships,
technological
support.
Frontiers in Oncology,
Год журнала:
2023,
Номер
12
Опубликована: Янв. 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.
Computational and Structural Biotechnology Journal,
Год журнала:
2022,
Номер
21, С. 134 - 149
Опубликована: Дек. 1, 2022
The
emerging
high-throughput
technologies
have
led
to
the
shift
in
design
of
translational
medicine
projects
towards
collecting
multi-omics
patient
samples
and,
consequently,
their
integrated
analysis.
However,
complexity
integrating
these
datasets
has
triggered
new
questions
regarding
appropriateness
available
computational
methods.
Currently,
there
is
no
clear
consensus
on
best
combination
omics
include
and
data
integration
methodologies
required
for
This
article
aims
guide
studies
field
types
method
choose.
We
review
articles
that
perform
multiple
measurements
from
samples.
identify
five
objectives
applications:
(i)
detect
disease-associated
molecular
patterns,
(ii)
subtype
identification,
(iii)
diagnosis/prognosis,
(iv)
drug
response
prediction,
(v)
understand
regulatory
processes.
describe
common
trends
selection
omic
combined
different
diseases.
To
choice
tools,
we
group
them
into
scientific
they
aim
address.
main
methods
adopted
achieve
present
examples
tools.
compare
tools
based
how
deal
with
challenges
comment
against
predefined
objective-specific
evaluation
criteria.
Finally,
discuss
downstream
analysis
further
extraction
novel
insights
datasets.
Frontiers in Genetics,
Год журнала:
2022,
Номер
13
Опубликована: Янв. 27, 2022
Cancer
is
defined
as
a
large
group
of
diseases
that
associated
with
abnormal
cell
growth,
uncontrollable
division,
and
may
tend
to
impinge
on
other
tissues
the
body
by
different
mechanisms
through
metastasis.
What
makes
cancer
so
important
incidence
rate
growing
worldwide
which
can
have
major
health,
economic,
even
social
impacts
both
patients
governments.
Thereby,
early
prognosis,
diagnosis,
treatment
play
crucial
role
at
front
line
combating
cancer.
The
onset
progression
occur
under
influence
complicated
some
alterations
in
level
genome,
proteome,
transcriptome,
metabolome
etc.
Consequently,
advent
omics
science
its
broad
research
branches
(such
genomics,
proteomics,
transcriptomics,
metabolomics,
forth)
revolutionary
biological
approaches
opened
new
doors
comprehensive
perception
landscape.
Due
complexities
formation
development
cancer,
study
underlying
has
gone
beyond
just
one
field
arena.
Therefore,
making
connection
between
resultant
data
from
examining
them
multi-omics
pave
way
for
facilitating
discovery
novel
prognostic,
diagnostic,
therapeutic
approaches.
As
volume
complexity
studies
are
increasing
dramatically,
use
leading-edge
technologies
such
machine
learning
promising
assessments
data.
Machine
categorized
subset
artificial
intelligence
aims
parsing,
classification,
pattern
identification
applying
statistical
methods
algorithms.
This
acquired
knowledge
subsequently
allows
computers
learn
improve
accurate
predictions
experiences
processing.
In
this
context,
application
learning,
computational
technology
offers
opportunities
achieving
in-depth
analysis
studies.
it
be
concluded
roles
fight
against
Frontiers in Artificial Intelligence,
Год журнала:
2023,
Номер
6
Опубликована: Фев. 9, 2023
Biological
systems
function
through
complex
interactions
between
various
'omics
(biomolecules),
and
a
more
complete
understanding
of
these
is
only
possible
an
integrated,
multi-omic
perspective.
This
has
presented
the
need
for
development
integration
approaches
that
are
able
to
capture
complex,
often
non-linear,
define
biological
adapted
challenges
combining
heterogenous
data
across
'omic
views.
A
principal
challenge
missing
because
all
biomolecules
not
measured
in
samples.
Due
either
cost,
instrument
sensitivity,
or
other
experimental
factors,
sample
may
be
one
techologies.
Recent
methodological
developments
artificial
intelligence
statistical
learning
have
greatly
facilitated
analyses
multi-omics
data,
however
many
techniques
assume
access
completely
observed
data.
subset
methods
incorporate
mechanisms
handling
partially
samples,
focus
this
review.
We
describe
recently
developed
approaches,
noting
their
primary
use
cases
highlighting
each
method's
approach
additionally
provide
overview
traditional
workflows
limitations;
we
discuss
potential
avenues
further
as
well
how
issue
its
current
solutions
generalize
beyond
context.
Nature Biotechnology,
Год журнала:
2023,
Номер
41(3), С. 399 - 408
Опубликована: Янв. 2, 2023
The
application
of
multiple
omics
technologies
in
biomedical
cohorts
has
the
potential
to
reveal
patient-level
disease
characteristics
and
individualized
response
treatment.
However,
scale
heterogeneous
nature
multi-modal
data
makes
integration
inference
a
non-trivial
task.
We
developed
deep-learning-based
framework,
multi-omics
variational
autoencoders
(MOVE),
integrate
such
applied
it
cohort
789
people
with
newly
diagnosed
type
2
diabetes
deep
phenotyping
from
DIRECT
consortium.
Using
silico
perturbations,
we
identified
drug-omics
associations
across
datasets
for
20
most
prevalent
drugs
given
substantially
higher
sensitivity
than
univariate
statistical
tests.
From
these,
among
others,
novel
between
metformin
gut
microbiota
as
well
opposite
molecular
responses
two
statins,
simvastatin
atorvastatin.
used
quantify
drug-drug
similarities,
assess
degree
polypharmacy
conclude
that
drug
effects
are
distributed
modalities.