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
Protein-protein
interactions
(PPIs)
are
ubiquitous
in
biology,
yet
a
comprehensive
structural
characterization
of
the
PPIs
underlying
cellular
processes
is
lacking.
AlphaFold-Multimer
(AF-M)
has
potential
to
fill
this
knowledge
gap,
but
standard
AF-M
confidence
metrics
do
not
reliably
separate
relevant
from
an
abundance
false
positive
predictions.
To
address
limitation,
we
used
machine
learning
on
curated
datasets
train
structure
prediction
and
omics-informed
classifier
(SPOC)
that
effectively
separates
true
predictions
PPIs,
including
proteome-wide
screens.
We
applied
SPOC
all-by-all
matrix
nearly
300
human
genome
maintenance
proteins,
generating
∼40,000
can
be
viewed
at
predictomes.org,
where
users
also
score
their
own
with
SPOC.
High-confidence
discovered
using
our
approach
enable
hypothesis
generation
maintenance.
Our
results
provide
framework
for
interpreting
large-scale
screens
help
lay
foundation
interactome.
The Innovation,
Год журнала:
2025,
Номер
6(1), С. 100750 - 100750
Опубликована: Янв. 1, 2025
Predicting
free
energy
changes
(ΔΔG)
is
essential
for
enhancing
our
understanding
of
protein
evolution
and
plays
a
pivotal
role
in
engineering
pharmaceutical
development.
While
traditional
methods
offer
valuable
insights,
they
are
often
constrained
by
computational
speed
reliance
on
biased
training
datasets.
These
constraints
become
particularly
evident
when
aiming
accurate
ΔΔG
predictions
across
diverse
array
sequences.
Herein,
we
introduce
Pythia,
self-supervised
graph
neural
network
specifically
designed
zero-shot
predictions.
Our
comparative
benchmarks
demonstrate
that
Pythia
outperforms
other
pretraining
models
force
field-based
approaches
while
also
exhibiting
competitive
performance
with
fully
supervised
models.
Notably,
shows
strong
correlations
achieves
remarkable
increase
up
to
105-fold.
We
further
validated
Pythia's
predicting
the
thermostabilizing
mutations
limonene
epoxide
hydrolase,
leading
higher
experimental
success
rates.
This
exceptional
efficiency
has
enabled
us
explore
26
million
high-quality
structures,
marking
significant
advancement
ability
navigate
sequence
space
enhance
relationships
between
genotype
phenotype.
In
addition,
established
web
server
at
https://pythia.wulab.xyz
allow
users
easily
perform
such
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
