Human Genomics,
Journal Year:
2022,
Volume and Issue:
16(1)
Published: July 25, 2022
Genomics
is
advancing
towards
data-driven
science.
Through
the
advent
of
high-throughput
data
generating
technologies
in
human
genomics,
we
are
overwhelmed
with
heap
genomic
data.
To
extract
knowledge
and
pattern
out
this
data,
artificial
intelligence
especially
deep
learning
methods
has
been
instrumental.
In
current
review,
address
development
application
methods/models
different
subarea
genomics.
We
assessed
over-
under-charted
area
genomics
by
techniques.
Deep
algorithms
underlying
tools
have
discussed
briefly
later
part
review.
Finally,
about
late
genomic.
Conclusively,
review
timely
for
biotechnology
or
scientists
order
to
guide
them
why,
when
how
use
analyse
Nature Methods,
Journal Year:
2021,
Volume and Issue:
18(10), P. 1196 - 1203
Published: Oct. 1, 2021
Abstract
How
noncoding
DNA
determines
gene
expression
in
different
cell
types
is
a
major
unsolved
problem,
and
critical
downstream
applications
human
genetics
depend
on
improved
solutions.
Here,
we
report
substantially
prediction
accuracy
from
sequences
through
the
use
of
deep
learning
architecture,
called
Enformer,
that
able
to
integrate
information
long-range
interactions
(up
100
kb
away)
genome.
This
improvement
yielded
more
accurate
variant
effect
predictions
for
both
natural
genetic
variants
saturation
mutagenesis
measured
by
massively
parallel
reporter
assays.
Furthermore,
Enformer
learned
predict
enhancer–promoter
directly
sequence
competitively
with
methods
take
direct
experimental
data
as
input.
We
expect
these
advances
will
enable
effective
fine-mapping
disease
associations
provide
framework
interpret
cis
-regulatory
evolution.
Genome Medicine,
Journal Year:
2019,
Volume and Issue:
11(1)
Published: Nov. 19, 2019
Abstract
Artificial
intelligence
(AI)
is
the
development
of
computer
systems
that
are
able
to
perform
tasks
normally
require
human
intelligence.
Advances
in
AI
software
and
hardware,
especially
deep
learning
algorithms
graphics
processing
units
(GPUs)
power
their
training,
have
led
a
recent
rapidly
increasing
interest
medical
applications.
In
clinical
diagnostics,
AI-based
vision
approaches
poised
revolutionize
image-based
while
other
subtypes
begun
show
similar
promise
various
diagnostic
modalities.
some
areas,
such
as
genomics,
specific
type
algorithm
known
used
process
large
complex
genomic
datasets.
this
review,
we
first
summarize
main
classes
problems
well
suited
solve
describe
benefit
from
these
solutions.
Next,
focus
on
emerging
methods
for
including
variant
calling,
genome
annotation
classification,
phenotype-to-genotype
correspondence.
Finally,
end
with
discussion
future
potential
individualized
medicine
applications,
risk
prediction
common
diseases,
challenges,
limitations,
biases
must
be
carefully
addressed
successful
deployment
particularly
those
utilizing
genetics
genomics
data.
Cell Reports,
Journal Year:
2020,
Volume and Issue:
31(7), P. 107663 - 107663
Published: May 1, 2020
Algorithms
that
accurately
predict
gene
structure
from
primary
sequence
alone
were
transformative
for
annotating
the
human
genome.
Can
we
also
expression
levels
of
genes
based
solely
on
genome
sequence?
Here,
sought
to
apply
deep
convolutional
neural
networks
toward
goal.
Surprisingly,
a
model
includes
only
promoter
sequences
and
features
associated
with
mRNA
stability
explains
59%
71%
variation
in
steady-state
mouse,
respectively.
This
model,
termed
Xpresso,
more
than
doubles
accuracy
alternative
sequence-based
models
isolates
rules
as
predictive
relying
chromatic
immunoprecipitation
sequencing
(ChIP-seq)
data.
Xpresso
recapitulates
genome-wide
patterns
transcriptional
activity,
its
residuals
can
be
used
quantify
influence
enhancers,
heterochromatic
domains,
microRNAs.
Model
interpretation
reveals
promoter-proximal
CpG
dinucleotides
strongly
activity.
Looking
forward,
propose
cell-type-specific
gene-expression
predictions
grand
challenge
field.
