Computer Science & IT Research Journal,
Год журнала:
2023,
Номер
4(3), С. 185 - 199
Опубликована: Дек. 3, 2023
This
paper
examines
the
role
of
Artificial
Intelligence
(AI)
in
developing
countries,
focusing
on
bridging
gap
between
its
vast
potential
and
effective
implementation.
As
AI
technologies
advance
globally,
their
impact
socio-economic
development
becomes
increasingly
critical,
particularly
regions
with
diverse
challenges
opportunities.
The
study
investigates
current
landscape
adoption
analyzing
benefits,
challenges,
ethical
considerations.
Through
a
comprehensive
review
literature
case
studies,
explores
strategies
solutions
for
harnessing
AI's
transformative
power
sectors
such
as
healthcare,
agriculture,
education.
findings
emphasize
importance
capacity
building,
public-private
partnerships,
tailored
policy
frameworks
to
address
infrastructure
limitations
skill
gaps.
research
contributes
nuanced
understanding
opportunities
complexities
surrounding
implementation
providing
insights
policymakers,
practitioners,
scholars
seeking
navigate
this
evolving
technological
landscape
Keywords:
Intelligence;
Global
Connectivity;
Emerging
Technologies;
Organizational
Resilience;
Sustainable
Growth;
Developing
Country.
IEEE Access,
Год журнала:
2023,
Номер
11, С. 12765 - 12795
Опубликована: Янв. 1, 2023
The
rapid
progress
in
digitalization
and
automation
have
led
to
an
accelerated
growth
healthcare,
generating
novel
models
that
are
creating
new
channels
for
rendering
treatment
at
reduced
cost.
Metaverse
is
emerging
technology
the
digital
space
which
has
huge
potential
enabling
realistic
experiences
patients
as
well
medical
practitioners.
a
confluence
of
multiple
technologies
such
artificial
intelligence,
virtual
reality,
augmented
internet
devices,
robotics,
quantum
computing,
etc.
through
directions
providing
quality
healthcare
services
can
be
explored.
amalgamation
these
ensures
immersive,
intimate
personalized
patient
care.
It
also
provides
adaptive
intelligent
solutions
eliminates
barriers
between
providers
receivers.
This
article
comprehensive
review
emphasizing
on
state
art,
adopt
applications,
related
projects.
issues
adaptation
applications
identified
plausible
highlighted
part
future
research
directions.
Pharmaceuticals,
Год журнала:
2023,
Номер
16(6), С. 891 - 891
Опубликована: Июнь 18, 2023
Artificial
intelligence
(AI)
has
the
potential
to
revolutionize
drug
discovery
process,
offering
improved
efficiency,
accuracy,
and
speed.
However,
successful
application
of
AI
is
dependent
on
availability
high-quality
data,
addressing
ethical
concerns,
recognition
limitations
AI-based
approaches.
In
this
article,
benefits,
challenges
drawbacks
in
field
are
reviewed,
possible
strategies
approaches
for
overcoming
present
obstacles
proposed.
The
use
data
augmentation,
explainable
AI,
integration
with
traditional
experimental
methods,
as
well
advantages
pharmaceutical
research
also
discussed.
Overall,
review
highlights
provides
insights
into
opportunities
realizing
its
field.
Note
from
human-authors:
This
article
was
created
test
ability
ChatGPT,
a
chatbot
based
GPT-3.5
language
model,
assist
human
authors
writing
articles.
text
generated
by
following
our
instructions
(see
Supporting
Information)
used
starting
point,
automatically
generate
content
evaluated.
After
conducting
thorough
review,
practically
rewrote
manuscript,
striving
maintain
balance
between
original
proposal
scientific
criteria.
using
purpose
discussed
last
section.
Signal Transduction and Targeted Therapy,
Год журнала:
2023,
Номер
8(1)
Опубликована: Сен. 21, 2023
Abstract
Ferroptosis
is
an
iron-dependent
form
of
regulated
cell
death
with
distinct
characteristics,
including
altered
iron
homeostasis,
reduced
defense
against
oxidative
stress,
and
abnormal
lipid
peroxidation.
Recent
studies
have
provided
compelling
evidence
supporting
the
notion
that
ferroptosis
plays
a
key
pathogenic
role
in
many
diseases
such
as
various
cancer
types,
neurodegenerative
disease,
involving
tissue
and/or
organ
injury,
inflammatory
infectious
diseases.
Although
precise
regulatory
networks
underlie
are
largely
unknown,
particularly
respect
to
initiation
progression
diseases,
recognized
bona
fide
target
for
further
development
treatment
prevention
strategies.
Over
past
decade,
considerable
progress
has
been
made
developing
pharmacological
agonists
antagonists
these
ferroptosis-related
conditions.
Here,
we
provide
detailed
overview
our
current
knowledge
regarding
ferroptosis,
its
pathological
roles,
regulation
during
disease
progression.
Focusing
on
use
chemical
tools
preclinical
studies,
also
summarize
recent
advances
targeting
across
growing
spectrum
ferroptosis-associated
Finally,
discuss
new
challenges
opportunities
potential
strategy
treating
Bioengineering,
Год журнала:
2024,
Номер
11(4), С. 337 - 337
Опубликована: Март 29, 2024
As
healthcare
systems
around
the
world
face
challenges
such
as
escalating
costs,
limited
access,
and
growing
demand
for
personalized
care,
artificial
intelligence
(AI)
is
emerging
a
key
force
transformation.
This
review
motivated
by
urgent
need
to
harness
AI’s
potential
mitigate
these
issues
aims
critically
assess
integration
in
different
domains.
We
explore
how
AI
empowers
clinical
decision-making,
optimizes
hospital
operation
management,
refines
medical
image
analysis,
revolutionizes
patient
care
monitoring
through
AI-powered
wearables.
Through
several
case
studies,
we
has
transformed
specific
domains
discuss
remaining
possible
solutions.
Additionally,
will
methodologies
assessing
solutions,
ethical
of
deployment,
importance
data
privacy
bias
mitigation
responsible
technology
use.
By
presenting
critical
assessment
transformative
potential,
this
equips
researchers
with
deeper
understanding
current
future
impact
on
healthcare.
It
encourages
an
interdisciplinary
dialogue
between
researchers,
clinicians,
technologists
navigate
complexities
implementation,
fostering
development
AI-driven
solutions
that
prioritize
standards,
equity,
patient-centered
approach.
Abstract
Recent
rapid
biotechnological
breakthroughs
have
led
to
the
identification
of
complex
and
unique
molecular
features
that
drive
malignancies.
Precision
medicine
has
exploited
next-generation
sequencing
matched
targeted
therapy/immunotherapy
deployment
successfully
transform
outlook
for
several
fatal
cancers.
Tumor
liquid
biopsy
genomic
profiling
transcriptomic,
immunomic,
proteomic
interrogation
can
now
all
be
leveraged
optimize
therapy.
Multiple
new
trial
designs,
including
basket
umbrella
trials,
master
platform
N-of-1
patient-centric
studies,
are
beginning
supplant
standard
phase
I,
II,
III
protocols,
allowing
accelerated
drug
evaluation
approval
molecular-based
individualized
treatment.
Furthermore,
real-world
data,
as
well
exploitation
digital
apps
structured
observational
registries,
utilization
machine
learning
and/or
artificial
intelligence,
may
further
accelerate
knowledge
acquisition.
Overall,
clinical
trials
evolved,
shifting
from
tumor
type-centered
gene-directed
histology-agnostic
with
innovative
adaptive
designs
personalized
combination
treatment
strategies
tailored
individual
biomarker
profiles.
Some,
but
not
all,
novel
demonstrate
therapy
correlates
superior
outcomes
compared
non-matched
across
types
in
specific
To
improve
precision
paradigm,
strategy
matching
drugs
patients
based
on
should
implemented
earlier
disease
course,
cancers
comprehensive
multi-omic
(genomics,
transcriptomics,
proteomics,
immunomic)
profiling.
overcome
cancer
complexity,
moving
drug-centric
is
critical.
This
review
focuses
design,
advantages,
limitations,
challenges
a
spectrum
era
oncology.
Journal of Chemical Information and Modeling,
Год журнала:
2023,
Номер
63(9), С. 2628 - 2643
Опубликована: Апрель 26, 2023
Toxicity
prediction
is
a
critical
step
in
the
drug
discovery
process
that
helps
identify
and
prioritize
compounds
with
greatest
potential
for
safe
effective
use
humans,
while
also
reducing
risk
of
costly
late-stage
failures.
It
estimated
over
30%
candidates
are
discarded
owing
to
toxicity.
Recently,
artificial
intelligence
(AI)
has
been
used
improve
toxicity
as
it
provides
more
accurate
efficient
methods
identifying
potentially
toxic
effects
new
before
they
tested
human
clinical
trials,
thus
saving
time
money.
In
this
review,
we
present
an
overview
recent
advances
AI-based
prediction,
including
various
machine
learning
algorithms
deep
architectures,
six
major
properties
Tox21
assay
end
points.
Additionally,
provide
list
public
data
sources
useful
tools
research
community
highlight
challenges
must
be
addressed
enhance
model
performance.
Finally,
discuss
future
perspectives
prediction.
This
review
can
aid
researchers
understanding
pave
way
discovery.
Drug Discovery Today,
Год журнала:
2022,
Номер
27(6), С. 1560 - 1574
Опубликована: Фев. 22, 2022
The
year
2021
marks
the
125th
anniversary
of
Bayer
Chemical
Research
Laboratory
in
Wuppertal,
Germany.
A
significant
number
prominent
small-molecule
drugs,
from
Aspirin
to
Xarelto,
have
emerged
this
research
site.
In
review,
we
shed
light
on
historic
cornerstones
drug
research,
discussing
current
and
future
trends
discovery
as
well
providing
a
personal
outlook
with
focus
small
molecules.
Briefings in Bioinformatics,
Год журнала:
2023,
Номер
24(3)
Опубликована: Апрель 6, 2023
Abstract
Network
pharmacology
is
an
emerging
area
of
systematic
drug
research
that
attempts
to
understand
actions
and
interactions
with
multiple
targets.
has
changed
the
paradigm
from
‘one-target
one-drug’
highly
potent
‘multi-target
drug’.
Despite
that,
this
synergistic
approach
currently
facing
many
challenges
particularly
mining
effective
information
such
as
targets,
mechanism
action,
organism
interaction
massive,
heterogeneous
data.
To
overcome
bottlenecks
in
multi-target
discovery,
computational
algorithms
are
welcomed
by
scientific
community.
Machine
learning
(ML)
especially
its
subfield
deep
(DL)
have
seen
impressive
advances.
Techniques
developed
within
these
fields
now
able
analyze
learn
huge
amounts
data
disparate
formats.
In
terms
network
pharmacology,
ML
can
improve
discovery
decision
making
big
Opportunities
apply
occur
all
stages
research.
Examples
include
screening
biologically
active
small
molecules,
target
identification,
metabolic
pathways
protein–protein
analysis,
hub
gene
analysis
finding
binding
affinity
between
compounds
proteins.
This
review
summarizes
premier
algorithmic
concepts
forecasts
future
opportunities,
potential
applications
well
several
remaining
implementing
pharmacology.
our
knowledge,
study
provides
first
comprehensive
assessment
approaches
we
hope
it
encourages
additional
efforts
toward
development
acceptance
pharmaceutical
industry.