Briefings in Bioinformatics,
Год журнала:
2024,
Номер
26(1)
Опубликована: Ноя. 22, 2024
Abstract
Understanding
the
genetic
basis
of
phenotypic
variation
is
fundamental
to
biology.
Here
we
introduce
GAP,
a
novel
machine
learning
framework
for
predicting
binary
phenotypes
from
gaps
in
multi-species
sequence
alignments.
GAP
employs
neural
network
predict
presence
or
absence
solely
alignment
gaps,
contrasting
with
existing
tools
that
require
additional
and
often
inaccessible
input
data.
can
be
applied
three
distinct
problems:
species
known
associated
genomic
regions,
pinpointing
positions
within
such
regions
are
important
phenotypes,
extracting
sets
candidate
phenotypes.
We
showcase
utility
by
exploiting
well-known
association
between
L-gulonolactone
oxidase
(Gulo)
gene
vitamin
C
synthesis,
demonstrating
its
perfect
prediction
accuracy
34
vertebrates.
This
exceptional
performance
also
applies
more
generally,
achieving
high
power
on
large
simulated
dataset.
Moreover,
predictions
synthesis
unknown
status
mirror
their
phylogenetic
relationships,
predictive
importance
consistent
those
identified
previous
studies.
Last,
genome-wide
application
identifies
many
genes
may
analysis
these
candidates
uncovers
functional
enrichment
immunity,
widely
recognized
role
C.
Hence,
represents
simple
yet
useful
tool
genotype–phenotype
associations
addressing
diverse
evolutionary
questions
data
available
broad
range
study
systems.
BMC Bioinformatics,
Год журнала:
2025,
Номер
26(1)
Опубликована: Янв. 13, 2025
Drug-target
interactions
(DTIs)
are
pivotal
in
drug
discovery
and
development,
their
accurate
identification
can
significantly
expedite
the
process.
Numerous
DTI
prediction
methods
have
emerged,
yet
many
fail
to
fully
harness
feature
information
of
drugs
targets
or
address
issue
redundancy.
We
aim
refine
accuracy
by
eliminating
redundant
features
capitalizing
on
node
topological
structure
enhance
extraction.
To
achieve
this,
we
introduce
a
PCA-augmented
multi-layer
heterogeneous
graph-based
network
that
concentrates
key
throughout
encoding-decoding
phase.
Our
approach
initiates
with
construction
graph
from
various
similarity
metrics,
which
is
then
encoded
via
neural
network.
concatenate
integrate
resultant
representation
vectors
merge
multi-level
information.
Subsequently,
principal
component
analysis
applied
distill
most
informative
features,
random
forest
algorithm
employed
for
final
decoding
integrated
data.
method
outperforms
six
baseline
models
terms
accuracy,
as
demonstrated
extensive
experimentation.
Comprehensive
ablation
studies,
visualization
results,
in-depth
case
analyses
further
validate
our
framework's
efficacy
interpretability,
providing
novel
tool
integrates
multimodal
features.
Computers in Biology and Medicine,
Год журнала:
2025,
Номер
188, С. 109845 - 109845
Опубликована: Фев. 20, 2025
In
computational
biology,
accurate
RNA
structure
prediction
offers
several
benefits,
including
facilitating
a
better
understanding
of
functions
and
RNA-based
drug
design.
Implementing
deep
learning
techniques
for
has
led
tremendous
progress
in
this
field,
resulting
significant
improvements
accuracy.
This
comprehensive
review
aims
to
provide
an
overview
the
diverse
strategies
employed
predicting
secondary
structures,
emphasizing
methods.
The
article
categorizes
discussion
into
three
main
dimensions:
feature
extraction
methods,
existing
state-of-the-art
model
architectures,
approaches.
We
present
comparative
analysis
various
models
highlighting
their
strengths
weaknesses.
Finally,
we
identify
gaps
literature,
discuss
current
challenges,
suggest
future
approaches
enhance
performance
applicability
tasks.
provides
deeper
insight
subject
paves
way
further
dynamic
intersection
life
sciences
artificial
intelligence.
Clinical and Translational Discovery,
Год журнала:
2024,
Номер
4(3)
Опубликована: Июнь 1, 2024
Abstract
Combination
therapy
has
emerged
as
an
efficacy
strategy
for
treating
complex
diseases.
Its
potential
to
overcome
drug
resistance
and
minimize
toxicity
makes
it
highly
desirable.
However,
the
vast
number
of
pairs
presents
a
significant
challenge,
rendering
exhaustive
clinical
testing
impractical.
In
recent
years,
deep
learning‐based
methods
have
promising
tools
predicting
synergistic
combinations.
This
review
aims
provide
comprehensive
overview
applying
diverse
deep‐learning
architectures
combination
prediction.
commences
by
elucidating
quantitative
measures
employed
assess
synergy.
Subsequently,
we
delve
into
various
currently
Finally,
concludes
outlining
key
challenges
facing
learning
approaches
proposes
future
research.
Drug-target
binding
affinity
(DTA)
prediction
is
vital
in
drug
discovery
and
repositioning,
more
researchers
are
beginning
to
focus
on
this.
Many
effective
methods
have
been
proposed.
However,
some
current
certain
shortcomings
focusing
important
nodes
molecular
graphs
dealing
with
complex
structural
molecules.
In
particular,
when
considering
substructures
molecules,
they
may
not
be
able
fully
explore
the
potential
relationships
between
different
parts.
addition,
protein
structures,
ignore
connections
amino
acid
fragments
that
far
apart
sequence
but
work
synergistically
function.
this
paper,
we
propose
a
new
method,
called
GS-DTA,
for
predicting
DTA
based
graph
models.
GS-DTA
takes
simplified
input
line
system
(SMILES)
of
as
input.
First,
each
modeled
graph,
which
vertex
an
atom
edge
represents
interaction
atoms.
Then
GATv2-GCN
three-layer
GCN
networks
used
extract
features
drug.
enhances
model's
ability
by
assigning
dynamic
attention
scores,
improves
learning
structure's
intricate
patterns.
Besides,
The
can
captures
hierarchical
through
deeper
propagation
feature
transformation.
Meanwhile,
protein,
framework
combining
CNN,
Bi-LSTM,
Transformer
contextual
information
sequences,
combination
help
understand
comprehensive
detailed
protein.
Finally,
obtained
vectors
combined
predict
connected
layer.
source
code
downloaded
from
https://github.com/zhuziguang/GS-DTA
.
results
show
achieves
good
performance
terms
MSE,
CI,
r2m
Davis
KIBA
datasets,
improving
accuracy
prediction.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 19, 2025
A
bstract
Predicting
synergistic
cancer
drug
combinations
through
computational
methods
offers
a
scalable
approach
to
creating
therapies
that
are
more
effective
and
less
toxic.
However,
most
algorithms
focus
solely
on
synergy
without
considering
toxicity
when
selecting
optimal
combinations.
In
the
absence
of
combinatorial
assays,
few
models
use
penalties
balance
high
with
lower
toxicity.
these
have
not
been
explicitly
validated
against
known
drug-drug
interactions.
this
study,
we
examine
whether
scores
metrics
correlate
adverse
While
some
show
trends
levels,
our
results
reveal
significant
limitations
in
using
them
as
penalties.
These
findings
highlight
challenges
incorporating
into
prediction
frameworks
suggest
advancing
field
requires
comprehensive
combination
data.
Briefings in Bioinformatics,
Год журнала:
2024,
Номер
25(4)
Опубликована: Май 23, 2024
Artificial
intelligence
(AI)
powered
drug
development
has
received
remarkable
attention
in
recent
years.
It
addresses
the
limitations
of
traditional
experimental
methods
that
are
costly
and
time-consuming.
While
there
have
been
many
surveys
attempting
to
summarize
related
research,
they
only
focus
on
general
AI
or
specific
aspects
such
as
natural
language
processing
graph
neural
network.
Considering
rapid
advance
computer
vision,
using
molecular
image
enable
appears
be
a
more
intuitive
effective
approach
since
each
chemical
substance
unique
visual
representation.
In
this
paper,
we
provide
first
survey
image-based
representation
for
development.
The
proposes
taxonomy
based
learning
paradigms
vision
reviews
large
number
corresponding
papers,
highlighting
contributions
Besides,
discuss
applications,
future
directions
field.
We
hope
could
offer
valuable
insight
into
use
context