Briefings in Bioinformatics,
Год журнала:
2024,
Номер
25(5)
Опубликована: Июль 25, 2024
Abstract
Genome-wide
association
studies
(GWAS)
serve
as
a
crucial
tool
for
identifying
genetic
factors
associated
with
specific
traits.
However,
ethical
constraints
prevent
the
direct
exchange
of
information,
prompting
need
privacy
preservation
solutions.
To
address
these
issues,
earlier
works
are
based
on
cryptographic
mechanisms
such
homomorphic
encryption,
secure
multi-party
computing,
and
differential
privacy.
Very
recently,
federated
learning
has
emerged
promising
solution
enabling
collaborative
GWAS
computations.
This
work
provides
an
extensive
overview
existing
methods
preserving,
main
focus
distributed
approaches.
survey
comprehensive
analysis
challenges
faced
by
methods,
their
limitations,
insights
into
designing
efficient
Complex & Intelligent Systems,
Год журнала:
2025,
Номер
11(2)
Опубликована: Янв. 7, 2025
Multimodal
sentiment
analysis
(MSA)
is
crucial
in
human-computer
interaction.
Current
methods
use
simple
sub-models
for
feature
extraction,
neglecting
multi-scale
features
and
the
complexity
of
emotions.
Text,
visual,
audio
each
have
unique
characteristics
MSA,
with
text
often
providing
more
emotional
cues
due
to
its
rich
semantics.
However,
current
approaches
treat
modalities
equally,
not
maximizing
text's
advantages.
To
solve
these
problems,
we
propose
a
novel
method
named
text-based
multimodal
fusion
network
extraction
unsupervised
contrastive
learning
(TMFN).
Firstly,
an
innovative
pyramid-structured
method,
which
captures
modal
data
through
convolution
kernels
different
sizes
strengthens
key
channel
attention
mechanism.
Second,
design
module,
consists
gating
unit
(TGU)
channel-wise
transformer
(TCAT).
TGU
responsible
guiding
regulating
process
other
information,
while
TCAT
improves
model's
ability
capture
relationship
between
achieves
effective
Finally,
further
optimize
representation
fused
features,
introduce
deeply
explore
intrinsic
connection
features.
Experimental
results
show
that
our
proposed
model
outperforms
state-of-the-art
models
MSA
on
two
benchmark
datasets.
