NEK2
is
a
member
of
the
NEKs
family
and
plays
an
important
role
in
cell
mitosis.
Increasing
evidence
suggests
that
associated
with
development
multiple
tumors,
but
systematic
studies
cancer
are
still
lacking.
Therefore,
we
evaluated
prognostic
value
33
cancers
to
elucidate
potential
function
pan-cancers.
We
investigated
pan-cancers
utilizing
The
Cancer
Genome
Atlas
(TCGA)
Genotype-Tissue
Expression
(GTEx)
database.
Additionally,
analyzed
association
between
gene
expression
across
various
cancers,
protein
expression,
tumor
microenvironment
(TME),
drug
sensitivity
using
several
software
web
platforms.The
oncogenic
was
initially
explored
bioinformatics
methods.
Furthermore,
conducted
vitro
experiments
preliminarily
validate
cervical
cancer.
overexpressed
almost
all
mutation
poorer
prognosis.
In
addition,
correlation
immune
features
such
as
infiltration,
checkpoint
genes,
mutational
burden
(TMB),
Microsatellite
instability(MSI)
etc.
suggest
could
potentially
be
applied
immunotherapy
tumors.
may
pan-cancer
biomarker
immunotherapeutic
target
for
improving
efficacy
therapy.
Nucleic Acids Research,
Год журнала:
2024,
Номер
53(D1), С. D147 - D156
Опубликована: Ноя. 23, 2024
Abstract
MicroRNAs
(miRNAs)
are
small
non-coding
RNAs
(18–26
nucleotides)
that
regulate
gene
expression
by
interacting
with
target
mRNAs,
affecting
various
physiological
and
pathological
processes.
miRTarBase,
a
database
of
experimentally
validated
miRNA–target
interactions
(MTIs),
now
features
over
3
817
550
MTIs
from
13
690
articles,
significantly
expanding
its
previous
version.
The
updated
includes
miRNA
therapeutic
agents,
revealing
roles
in
drug
resistance
strategies.
It
also
highlights
miRNAs
as
predictive,
safety
monitoring
biomarkers
for
toxicity
assessment,
clinical
treatment
guidance
optimization.
expansion
miRNA–mRNA
miRNA–miRNA
networks
allows
the
identification
key
regulatory
genes
co-regulatory
miRNAs,
providing
deeper
insights
into
functions
critical
genes.
Information
on
oxidized
sequences
has
been
added,
shedding
light
how
oxidative
modifications
influence
targeting
regulation.
integration
LLAMA3
model
NLP
pipeline,
alongside
prompt
engineering,
enables
efficient
miRNA–disease
associations
without
large
training
datasets.
An
data
redesigned
user
interface
enhance
accessibility,
reinforcing
miRTarBase
an
essential
resource
molecular
oncology,
development
related
fields.
is
available
at
https://mirtarbase.cuhk.edu.cn/∼miRTarBase/miRTarBase_2025.
Briefings in Bioinformatics,
Год журнала:
2024,
Номер
25(3)
Опубликована: Март 27, 2024
Abstract
Cyclic
peptides
offer
a
range
of
notable
advantages,
including
potent
antibacterial
properties,
high
binding
affinity
and
specificity
to
target
molecules,
minimal
toxicity,
making
them
highly
promising
candidates
for
drug
development.
However,
comprehensive
database
that
consolidates
both
synthetically
derived
naturally
occurring
cyclic
is
conspicuously
absent.
To
address
this
void,
we
introduce
CyclicPepedia
(https://www.biosino.org/iMAC/cyclicpepedia/),
pioneering
encompasses
8744
known
peptides.
This
repository,
structured
as
composite
knowledge
network,
offers
wealth
information
encompassing
various
aspects
peptides,
such
peptides’
sources,
categorizations,
structural
characteristics,
pharmacokinetic
profiles,
physicochemical
patented
applications,
collection
crucial
publications.
Supported
by
user-friendly
retrieval
system
calculation
tools
specifically
designed
will
be
able
facilitate
advancements
in
peptide
Abstract
In
recent
years,
drug
repositioning
has
emerged
as
a
promising
alternative
to
the
time-consuming,
expensive
and
risky
process
of
developing
new
drugs
for
diseases.
However,
current
database
faces
several
issues,
including
insufficient
data
volume,
restricted
types,
algorithm
inaccuracies
resulting
from
neglect
multidimensional
or
heterogeneous
data,
lack
systematic
organization
literature
associated
with
repositioning,
limited
analytical
capabilities
user-unfriendly
webpage
interfaces.
Hence,
we
have
established
first
all-encompassing
called
DrugRepoBank,
consisting
two
main
modules:
‘Literature’
module
‘Prediction’
module.
The
serves
largest
repository
literature-supported
experimental
evidence,
encompassing
169
repositioned
134
articles
1
January
2000
July
2023.
employs
18
efficient
algorithms,
similarity-based,
artificial-intelligence-based,
signature-based
network-based
methods
predict
candidates.
DrugRepoBank
features
an
interactive
user-friendly
web
interface
offers
comprehensive
functionalities
such
bioinformatics
analysis
disease
signatures.
When
users
provide
information
about
drug,
target
interest,
indications
targets
proposes
that
bind
suggests
potential
queried
disease.
Additionally,
it
provides
basic
drugs,
diseases,
along
supporting
literature.
We
utilize
three
case
studies
demonstrate
feasibility
effectiveness
predictively
within
DrugRepoBank.
establishment
will
significantly
accelerate
pace
repositioning.
Database
URL:
https://awi.cuhk.edu.cn/DrugRepoBank
Advanced Science,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 3, 2025
Abstract
Unexpected
toxicity
has
become
a
significant
obstacle
to
drug
candidate
development,
accounting
for
30%
of
discovery
failures.
Traditional
assessment
through
animal
testing
is
costly
and
time‐consuming.
Big
data
artificial
intelligence
(AI),
especially
machine
learning
(ML),
are
robustly
contributing
innovation
progress
in
toxicology
research.
However,
the
optimal
AI
model
different
types
usually
varies,
making
it
essential
conduct
comparative
analyses
methods
across
domains.
The
diverse
sources
also
pose
challenges
researchers
focusing
on
specific
studies.
In
this
review,
10
categories
drug‐induced
examined,
summarizing
characteristics
applicable
ML
models,
including
both
predictive
interpretable
algorithms,
striking
balance
between
breadth
depth.
Key
databases
tools
used
prediction
highlighted,
toxicology,
chemical,
multi‐omics,
benchmark
databases,
organized
by
their
focus
function
clarify
roles
prediction.
Finally,
strategies
turn
into
opportunities
analyzed
discussed.
This
review
may
provide
with
valuable
reference
understanding
utilizing
available
resources
bridge
mechanistic
insights,
further
advance
application
drugs‐induced
Nucleic Acids Research,
Год журнала:
2024,
Номер
53(D1), С. D1372 - D1382
Опубликована: Сен. 13, 2024
Abstract
The
escalating
costs
and
high
failure
rates
have
decelerated
the
pace
of
drug
development,
which
amplifies
research
interests
in
developing
combinatorial/repurposed
drugs
understanding
off-target
adverse
reaction
(ADR).
In
other
words,
it
is
demanded
to
delineate
molecular
atlas
pharma-information
for
interactions.
However,
such
invaluable
data
were
inadequately
covered
by
existing
databases.
this
study,
a
major
update
was
thus
conducted
DrugMAP,
accumulated
(a)
20831
combinatorial
their
interacting
involving
1583
pharmacologically
important
molecules;
(b)
842
repurposed
with
795
(c)
3260
off-targets
relevant
ADRs
2731
(d)
various
types
pharmaceutical
information,
including
diverse
ADMET
properties,
versatile
diseases,
ADRs/off-targets.
With
growing
demands
discovering
therapies
rapidly
emerging
interest
AI-based
discovery,
DrugMAP
highly
expected
act
as
an
indispensable
supplement
databases
facilitating
accessible
at:
https://idrblab.org/drugmap/.
Briefings in Bioinformatics,
Год журнала:
2024,
Номер
25(3)
Опубликована: Март 27, 2024
Antigen
presentation
on
MHC
class
II
(pMHCII
presentation)
plays
an
essential
role
in
the
adaptive
immune
response
to
extracellular
pathogens
and
cancerous
cells.
But
it
can
also
reduce
efficacy
of
large-molecule
drugs
by
triggering
anti-drug
response.
Significant
progress
has
been
made
pMHCII
modeling
due
collection
large-scale
pMHC
mass
spectrometry
datasets
(ligandomes)
advances
machine
learning.
Here,
we
develop
graph-pMHC,
a
graph
neural
network
approach
predict
presentation.
We
derive
adjacency
matrices
for
using
Alphafold2-multimer
address
peptide-MHC
binding
groove
alignment
problem
with
simple
enumeration
strategy.
demonstrate
that
graph-pMHC
dramatically
outperforms
methods
suboptimal
inductive
biases,
such
as
multilayer-perceptron-based
NetMHCIIpan-4.0
(+20.17%
absolute
average
precision).
Finally,
create
antibody
drug
immunogenicity
dataset
from
clinical
trial
data
method
measuring
anti-antibody
risk
models.
Our
model
increases
receiver
operating
characteristic
curve
(ROC)-area
under
ROC
(AUC)
2.57%
compared
just
filtering
peptides
hits
OASis
alone
predicting
immunogenicity.
