BMC Bioinformatics,
Год журнала:
2024,
Номер
25(1)
Опубликована: Апрель 29, 2024
Abstract
Many
important
biological
facts
have
been
found
as
single-cell
RNA
sequencing
(scRNA-seq)
technology
has
advanced.
With
the
use
of
this
technology,
it
is
now
possible
to
investigate
connections
among
individual
cells,
genes,
and
illnesses.
For
analysis
data,
clustering
frequently
used.
Nevertheless,
data
usually
contain
a
large
amount
noise
traditional
methods
are
sensitive
noise.
However,
acquiring
higher-order
spatial
information
from
alone
insufficient.
As
result,
getting
trustworthy
findings
challenging.
We
propose
Cauchy
hyper-graph
Laplacian
non-negative
matrix
factorization
(CHLNMF)
unique
approach
address
these
issues.
In
CHLNMF,
we
replace
measurement
based
on
Euclidean
distance
in
conventional
(NMF),
which
can
lessen
influence
noise,
with
loss
function
(CLF).
The
model
also
incorporates
constraint,
takes
into
account
high-order
link
samples.
CHLNMF
model's
best
solution
then
discovered
using
half-quadratic
optimization
approach.
Finally,
seven
scRNA-seq
datasets,
contrast
technique
other
nine
top
methods.
validity
our
was
established
by
experimental
outcomes.
ChemBioEng Reviews,
Год журнала:
2024,
Номер
11(4)
Опубликована: Июль 25, 2024
Abstract
Multidrug
resistance
(MDR)
remains
a
formidable
challenge
in
cancer
treatment,
necessitating
innovative
strategies
to
enhance
therapeutic
outcomes.
This
review
explores
the
potential
of
synergistic
gold
nanorod
(GNR)
therapy
(SGNRT)
utilizing
GNRs
as
multifunctional
platforms
for
co‐delivering
chemotherapy
and
thermal
therapies.
The
rational
design
SGNRT
systems
enables
targeted
payload
delivery,
circumvention
Adenosine
triphosphate
(ATP)‐binding
cassette
drug
efflux
transporters,
hyperthermia‐induced
chemo
sensitization.
In
vitro
studies
demonstrate
impact
overcoming
MDR,
emphasizing
its
enhanced
antitumor
efficacy.
However,
further
vivo
investigations
are
essential
assess
clinical
viability
this
nanoparticle
(NP)‐directed
approach
against
advanced
multidrug‐resistant
malignancies.
integration
holds
promise
advancing
precision
therapies
addressing
intricate
challenges
settings.
BMC Bioinformatics,
Год журнала:
2024,
Номер
25(1)
Опубликована: Апрель 29, 2024
Abstract
Many
important
biological
facts
have
been
found
as
single-cell
RNA
sequencing
(scRNA-seq)
technology
has
advanced.
With
the
use
of
this
technology,
it
is
now
possible
to
investigate
connections
among
individual
cells,
genes,
and
illnesses.
For
analysis
data,
clustering
frequently
used.
Nevertheless,
data
usually
contain
a
large
amount
noise
traditional
methods
are
sensitive
noise.
However,
acquiring
higher-order
spatial
information
from
alone
insufficient.
As
result,
getting
trustworthy
findings
challenging.
We
propose
Cauchy
hyper-graph
Laplacian
non-negative
matrix
factorization
(CHLNMF)
unique
approach
address
these
issues.
In
CHLNMF,
we
replace
measurement
based
on
Euclidean
distance
in
conventional
(NMF),
which
can
lessen
influence
noise,
with
loss
function
(CLF).
The
model
also
incorporates
constraint,
takes
into
account
high-order
link
samples.
CHLNMF
model's
best
solution
then
discovered
using
half-quadratic
optimization
approach.
Finally,
seven
scRNA-seq
datasets,
contrast
technique
other
nine
top
methods.
validity
our
was
established
by
experimental
outcomes.
