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Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks.
[severe combined immunodeficiency]
Identification
of
disease-causing
genes
among
a
large
number
of
candidates
is
a
fundamental
challenge
in
human
disease
studies
.
However
,
it
is
still
time-consuming
and
laborious
to
determine
the
real
disease-causing
genes
by
biological
experiments
.
With
the
advances
of
the
high
-throughput
techniques
,
a
large
number
of
protein-protein
interactions
have
been
produced
.
Therefore
,
to
address
this
issue
,
several
methods
based
on
protein
interaction
network
have
been
proposed
.
In
this
paper
,
we
propose
a
shortest
path-based
algorithm
,
named
SPranker
,
to
prioritize
disease-causing
genes
in
protein
interaction
networks
.
Considering
the
fact
that
diseases
with
similar
phenotypes
are
generally
caused
by
functionally
related
genes
,
we
further
propose
an
improved
algorithm
SPGOranker
by
integrating
the
semantic
similarity
of
GO
annotations
.
SPGOranker
not
only
considers
the
topological
similarity
between
protein
pairs
in
a
protein
interaction
network
but
also
takes
their
functional
similarity
into
account
.
The
proposed
algorithms
SPranker
and
SPGOranker
were
applied
to
1598
known
orphan
disease-causing
genes
from
172
orphan
diseases
and
compared
with
three
state
-of-the-art
approaches
,
ICN
,
VS
and
RWR
.
The
experimental
results
show
that
SPranker
and
SPGOranker
outperform
ICN
,
VS
,
and
RWR
for
the
prioritization
of
orphan
disease-causing
genes
.
Importantly
,
for
the
case
study
of
severe
combined
immunodeficiency
,
SPranker
and
SPGOranker
predict
several
novel
causal
genes
.
Diseases
Validation
Diseases presenting
"spranker and spgoranker predict several novel causal genes"
symptom
severe combined immunodeficiency
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