Exploring the Molecular Terrain: A Survey of Analytical Methods for Biological Network Analysis
Symmetry,
Journal Year:
2024,
Volume and Issue:
16(4), P. 462 - 462
Published: April 10, 2024
Biological
systems,
characterized
by
their
complex
interplay
of
symmetry
and
asymmetry,
operate
through
intricate
networks
interacting
molecules,
weaving
the
elaborate
tapestry
life.
The
exploration
these
networks,
aptly
termed
“molecular
terrain”,
is
pivotal
for
unlocking
mysteries
biological
processes
spearheading
development
innovative
therapeutic
strategies.
This
review
embarks
on
a
comprehensive
survey
analytical
methods
employed
in
network
analysis,
focusing
elucidating
roles
asymmetry
within
networks.
By
highlighting
strengths,
limitations,
potential
applications,
we
delve
into
reconstruction,
topological
analysis
with
an
emphasis
detection,
examination
dynamics,
which
together
reveal
nuanced
balance
between
stable,
symmetrical
configurations
dynamic,
asymmetrical
shifts
that
underpin
functionality.
equips
researchers
multifaceted
toolbox
designed
to
navigate
decipher
networks’
intricate,
balanced
landscape,
thereby
advancing
our
understanding
manipulation
systems.
Through
this
detailed
exploration,
aim
foster
significant
advancements
paving
way
novel
interventions
deeper
comprehension
molecular
underpinnings
Language: Английский
Integration of unpaired single cell omics data by deep transfer graph convolutional network
Yulong Kan,
No information about this author
Y. Qi,
No information about this author
Zhongxiao Zhang
No information about this author
et al.
PLoS Computational Biology,
Journal Year:
2025,
Volume and Issue:
21(1), P. e1012625 - e1012625
Published: Jan. 16, 2025
The
rapid
advance
of
large-scale
atlas-level
single
cell
RNA
sequences
and
single-cell
chromatin
accessibility
data
provide
extraordinary
avenues
to
broad
deep
insight
into
complex
biological
mechanism.
Leveraging
the
datasets
transfering
labels
from
scRNA-seq
scATAC-seq
will
empower
exploration
omics
data.
However,
current
label
transfer
methods
have
limited
performance,
largely
due
lower
capable
preserving
fine-grained
populations
intrinsic
or
extrinsic
heterogeneity
between
datasets.
Here,
we
present
a
robust
model
based
graph
convolutional
network,
scTGCN,
which
achieves
versatile
performance
in
variation,
while
achieving
integration
hundreds
thousands
cells
minutes
with
low
memory
consumption.
We
show
that
scTGCN
is
powerful
mouse
atlas
multimodal
generated
APSA-seq
CITE-seq.
Thus,
shows
high
accuracy
effectively
knowledge
across
different
modalities.
Language: Английский
Graph neural networks for single-cell omics data: a review of approaches and applications
Shiming Li,
No information about this author
Heyang Hua,
No information about this author
Shengquan Chen
No information about this author
et al.
Briefings in Bioinformatics,
Journal Year:
2025,
Volume and Issue:
26(2)
Published: March 1, 2025
Abstract
Rapid
advancement
of
sequencing
technologies
now
allows
for
the
utilization
precise
signals
at
single-cell
resolution
in
various
omics
studies.
However,
massive
volume,
ultra-high
dimensionality,
and
high
sparsity
nature
data
have
introduced
substantial
difficulties
to
traditional
computational
methods.
The
intricate
non-Euclidean
networks
intracellular
intercellular
signaling
molecules
within
datasets,
coupled
with
complex,
multimodal
structures
arising
from
multi-omics
joint
analysis,
pose
significant
challenges
conventional
deep
learning
operations
reliant
on
Euclidean
geometries.
Graph
neural
(GNNs)
extended
data,
allowing
cells
their
features
datasets
be
modeled
as
nodes
a
graph
structure.
GNNs
been
successfully
applied
across
broad
range
tasks
analysis.
In
this
survey,
we
systematically
review
107
successful
applications
six
variants
tasks.
We
begin
by
outlining
fundamental
principles
variants,
followed
systematic
GNN-based
models
epigenomics,
transcriptomics,
spatial
proteomics,
multi-omics.
each
section
dedicated
specific
type,
summarized
publicly
available
commonly
utilized
articles
reviewed
that
section,
totaling
77
datasets.
Finally,
summarize
potential
shortcomings
current
research
explore
directions
future
anticipate
will
serve
guiding
resource
researchers
deepen
application
omics.
Language: Английский
ZMGA: A ZINB-based multi-modal graph autoencoder enhancing topological consistency in single-cell clustering
Jiaxi Yao,
No information about this author
Lin Li,
No information about this author
Tongwen Xu
No information about this author
et al.
Biomedical Signal Processing and Control,
Journal Year:
2024,
Volume and Issue:
97, P. 106587 - 106587
Published: July 24, 2024
Language: Английский
TransGCN: a semi-supervised graph convolution network–based framework to infer protein translocations in spatio-temporal proteomics
Bing Wang,
No information about this author
Xiangzheng Zhang,
No information about this author
Xudong Han
No information about this author
et al.
Briefings in Bioinformatics,
Journal Year:
2024,
Volume and Issue:
25(2)
Published: Jan. 22, 2024
Abstract
Protein
subcellular
localization
(PSL)
is
very
important
in
order
to
understand
its
functions,
and
movement
between
niches
within
cells
plays
fundamental
roles
biological
process
regulation.
Mass
spectrometry–based
spatio-temporal
proteomics
technologies
can
help
provide
new
insights
of
protein
translocation,
but
bring
the
challenge
identifying
reliable
translocation
events
due
noise
interference
insufficient
data
mining.
We
propose
a
semi-supervised
graph
convolution
network
(GCN)–based
framework
termed
TransGCN
that
infers
from
proteomics.
Based
on
expanded
multiple
distance
features
joint
representations
proteins,
utilizes
GCN
enable
effective
knowledge
transfer
proteins
with
known
PSLs
for
predicting
translocation.
Our
results
demonstrate
outperforms
current
state-of-the-art
methods
translocations,
especially
coping
batch
effects.
It
also
exhibited
excellent
predictive
accuracy
PSL
prediction.
freely
available
GitHub
at
https://github.com/XuejiangGuo/TransGCN.
Language: Английский
scGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data
GigaScience,
Journal Year:
2024,
Volume and Issue:
13
Published: Jan. 1, 2024
Abstract
Background
Exploring
the
cellular
processes
of
genes
from
aspects
biological
networks
is
great
interest
to
understanding
properties
complex
diseases
and
systems.
Biological
networks,
such
as
protein–protein
interaction
gene
regulatory
provide
insights
into
molecular
basis
often
form
functional
clusters
in
different
tissue
disease
contexts.
Results
We
present
scGraph2Vec,
a
deep
learning
framework
for
generating
informative
embeddings.
scGraph2Vec
extends
variational
graph
autoencoder
integrates
single-cell
datasets
gene–gene
networks.
demonstrate
that
embeddings
are
biologically
interpretable
enable
identification
representing
or
tissue-specific
processes.
By
comparing
similar
tools,
we
showed
clearly
distinguished
aggregated
more
genes.
can
be
widely
applied
diverse
illustrated
generated
by
infer
disease-associated
genome-wide
association
study
data
(e.g.,
COVID-19
Alzheimer's
disease),
identify
additional
driver
lung
adenocarcinoma,
reveal
responsible
maintaining
transitioning
melanoma
cell
states.
Conclusions
not
only
reconstructs
but
also
obtains
latent
representation
implying
their
functions.
Language: Английский