Journal of Biomedical Science,
Год журнала:
2025,
Номер
32(1)
Опубликована: Фев. 7, 2025
Abstract
Artificial
intelligence
(AI)
has
emerged
as
a
transformative
force
in
precision
medicine,
revolutionizing
the
integration
and
analysis
of
health
records,
genetics,
immunology
data.
This
comprehensive
review
explores
clinical
applications
AI-driven
analytics
unlocking
personalized
insights
for
patients
with
autoimmune
rheumatic
diseases.
Through
synergistic
approach
integrating
AI
across
diverse
data
sets,
clinicians
gain
holistic
view
patient
potential
risks.
Machine
learning
models
excel
at
identifying
high-risk
patients,
predicting
disease
activity,
optimizing
therapeutic
strategies
based
on
clinical,
genomic,
immunological
profiles.
Deep
techniques
have
significantly
advanced
variant
calling,
pathogenicity
prediction,
splicing
analysis,
MHC-peptide
binding
predictions
genetics.
AI-enabled
including
dimensionality
reduction,
cell
population
identification,
sample
classification,
provides
unprecedented
into
complex
immune
responses.
The
highlights
real-world
examples
medicine
platforms
decision
support
tools
rheumatology.
Evaluation
outcomes
demonstrates
benefits
impact
these
approaches
care.
However,
challenges
such
quality,
privacy,
clinician
trust
must
be
navigated
successful
implementation.
future
lies
continued
research,
development,
to
unlock
care
drive
innovation
Nucleic Acids Research,
Год журнала:
2023,
Номер
52(D1), С. D672 - D678
Опубликована: Ноя. 6, 2023
The
Reactome
Knowledgebase
(https://reactome.org),
an
Elixir
and
GCBR
core
biological
data
resource,
provides
manually
curated
molecular
details
of
a
broad
range
normal
disease-related
processes.
Processes
are
annotated
as
ordered
network
transformations
in
single
consistent
model.
thus
functions
both
digital
archive
human
processes
tool
for
discovering
functional
relationships
such
gene
expression
profiles
or
somatic
mutation
catalogs
from
tumor
cells.
Here
we
review
progress
towards
annotation
the
entire
proteome,
targeted
disease-causing
genetic
variants
proteins
small-molecule
drugs
pathway
context,
supporting
explicit
cell-
tissue-specific
pathways.
Finally,
briefly
discuss
issues
involved
making
more
fully
interoperable
with
other
related
resources
Gene
Ontology
maintaining
resulting
community
resource
network.
ACS Central Science,
Год журнала:
2024,
Номер
10(2), С. 226 - 241
Опубликована: Фев. 5, 2024
Enzymes
can
be
engineered
at
the
level
of
their
amino
acid
sequences
to
optimize
key
properties
such
as
expression,
stability,
substrate
range,
and
catalytic
efficiency-or
even
unlock
new
activities
not
found
in
nature.
Because
search
space
possible
proteins
is
vast,
enzyme
engineering
usually
involves
discovering
an
starting
point
that
has
some
desired
activity
followed
by
directed
evolution
improve
its
"fitness"
for
a
application.
Recently,
machine
learning
(ML)
emerged
powerful
tool
complement
this
empirical
process.
ML
models
contribute
(1)
discovery
functional
annotation
known
protein
or
generating
novel
with
functions
(2)
navigating
fitness
landscapes
optimization
mappings
between
associated
values.
In
Outlook,
we
explain
how
complements
discuss
future
potential
improved
outcomes.
Frontiers in Bioengineering and Biotechnology,
Год журнала:
2024,
Номер
11
Опубликована: Янв. 8, 2024
Clustered
regularly
interspaced
short
palindromic
repeat
(CRISPR)-based
genome
editing
(GED)
technologies
have
unlocked
exciting
possibilities
for
understanding
genes
and
improving
medical
treatments.
On
the
other
hand,
Artificial
intelligence
(AI)
helps
achieve
more
precision,
efficiency,
affordability
in
tackling
various
diseases,
like
Sickle
cell
anemia
or
Thalassemia.
AI
models
been
use
designing
guide
RNAs
(gRNAs)
CRISPR-Cas
systems.
Tools
DeepCRISPR,
CRISTA,
DeepHF
capability
to
predict
optimal
a
specified
target
sequence.
These
predictions
take
into
account
multiple
factors,
including
genomic
context,
Cas
protein
type,
desired
mutation
on-target/off-target
scores,
potential
off-target
sites,
impacts
of
on
gene
function
phenotype.
aid
optimizing
different
technologies,
such
as
base,
prime,
epigenome
editing,
which
are
advanced
techniques
introduce
precise
programmable
changes
DNA
sequences
without
relying
homology-directed
repair
pathway
donor
templates.
Furthermore,
AI,
collaboration
with
precision
medicine,
enables
personalized
treatments
based
genetic
profiles.
analyzes
patients'
data
identify
mutations,
variations,
biomarkers
associated
diseases
Cancer,
Diabetes,
Alzheimer's,
etc.
However,
several
challenges
persist,
high
costs,
suitable
delivery
methods
CRISPR
cargoes,
ensuring
safety
clinical
applications.
This
review
explores
AI's
contribution
CRISPR-based
addresses
existing
challenges.
It
also
discusses
areas
future
research
AI-driven
technologies.
The
integration
opens
up
new
genetics,
biomedicine,
healthcare,
significant
implications
human
health.
BMC Medical Genomics,
Год журнала:
2024,
Номер
17(1)
Опубликована: Янв. 29, 2024
Abstract
Whole
genome
sequencing
(WGS)
is
becoming
the
preferred
method
for
molecular
genetic
diagnosis
of
rare
and
unknown
diseases
identification
actionable
cancer
drivers.
Compared
to
other
methods,
WGS
captures
most
genomic
variation
eliminates
need
sequential
testing.
Whereas,
laboratory
requirements
are
similar
conventional
genetics,
amount
data
large
requires
a
comprehensive
computational
storage
infrastructure
in
order
facilitate
processing
within
clinically
relevant
timeframe.
The
output
single
analyses
roughly
5
MIO
variants
interpretation
involves
specialized
staff
collaborating
with
clinical
specialists
provide
standard
care
reports.
Although
field
continuously
refining
standards
variant
classification,
there
still
unresolved
issues
associated
application.
review
provides
an
overview
practice
-
describing
technology
current
applications
as
well
challenges
connected
processing,
reporting.
Cancer Discovery,
Год журнала:
2024,
Номер
14(5), С. 711 - 726
Опубликована: Март 21, 2024
Artificial
intelligence
(AI)
in
oncology
is
advancing
beyond
algorithm
development
to
integration
into
clinical
practice.
This
review
describes
the
current
state
of
field,
with
a
specific
focus
on
integration.
AI
applications
are
structured
according
cancer
type
and
domain,
focusing
four
most
common
cancers
tasks
detection,
diagnosis,
treatment.
These
encompass
various
data
modalities,
including
imaging,
genomics,
medical
records.
We
conclude
summary
existing
challenges,
evolving
solutions,
potential
future
directions
for
field.
Biomolecules,
Год журнала:
2024,
Номер
14(3), С. 339 - 339
Опубликована: Март 12, 2024
Recent
advancements
in
AI-driven
technologies,
particularly
protein
structure
prediction,
are
significantly
reshaping
the
landscape
of
drug
discovery
and
development.
This
review
focuses
on
question
how
these
technological
breakthroughs,
exemplified
by
AlphaFold2,
revolutionizing
our
understanding
function
changes
underlying
cancer
improve
approaches
to
counter
them.
By
enhancing
precision
speed
at
which
targets
identified
candidates
can
be
designed
optimized,
technologies
streamlining
entire
development
process.
We
explore
use
AlphaFold2
development,
scrutinizing
its
efficacy,
limitations,
potential
challenges.
also
compare
with
other
algorithms
like
ESMFold,
explaining
diverse
methodologies
employed
this
field
practical
effects
differences
for
application
specific
algorithms.
Additionally,
we
discuss
broader
applications
including
prediction
complex
structures
generative
design
novel
proteins.
