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t
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l J
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Art
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stati
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tely
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las
sify
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p
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te
rs
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s
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sig
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ifi
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a
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t
fo
c
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s
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io
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fo
rm
a
ti
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s
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se
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h
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g
h
n
u
m
e
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s
st
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d
ies
h
a
v
e
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tt
e
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p
ted
t
o
a
d
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re
ss
th
is
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h
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ll
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g
e
,
th
e
p
e
rf
o
r
m
a
n
c
e
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isti
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m
e
th
o
d
s
stil
l
le
a
v
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ro
o
m
fo
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imp
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m
e
n
t
th
is
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d
y
,
sta
ti
stica
l
fe
a
tu
re
a
n
a
ly
sis
h
a
s
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e
e
n
a
p
p
li
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d
to
th
e
fe
a
tu
re
s
th
a
t
h
a
v
e
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e
n
d
e
v
e
l
o
p
e
d
i
n
o
u
r
p
re
v
i
o
u
s
wo
r
k
.
Th
is
a
p
p
ro
a
c
h
e
x
trac
ted
a
d
d
it
i
o
n
a
l
in
f
o
rm
a
ti
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e
f
e
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tu
re
s
fro
m
b
a
sic
se
q
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e
n
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e
c
h
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ra
c
teristics
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n
d
th
e
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u
se
d
t
h
e
m
to
g
e
th
e
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h
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rig
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l
a
n
d
n
e
wly
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g
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re
d
fe
a
tu
re
s.
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izin
g
sta
ti
stica
l
fe
a
tu
re
a
n
a
ly
s
is
e
n
h
a
n
c
e
d
k
e
y
p
a
tt
e
rn
s,
wh
ich
l
e
a
d
to
a
n
imp
ro
v
e
m
e
n
t
in
t
h
e
a
c
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u
ra
c
y
o
f
t
h
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p
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o
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o
ter
c
las
sifica
ti
o
n
.
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su
lt
s
d
e
m
o
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stra
ted
t
h
a
t
o
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r
p
r
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t
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se
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y
b
a
sic
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a
tu
re
s.
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h
e
v
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l
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f
th
e
a
re
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d
e
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th
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(AU
C)
o
f
0
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8
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a
c
h
iev
e
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wh
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n
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si
n
g
t
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e
c
o
m
b
in
e
d
fe
a
tu
re
se
t
c
o
n
firme
d
th
e
e
ffe
c
ti
v
e
n
e
ss
o
f
o
u
r
a
p
p
ro
a
c
h
.
F
u
rth
e
rm
o
re
,
t
h
e
AU
C
v
a
lu
e
re
a
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h
e
d
1
w
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e
n
t
h
e
se
o
p
ti
m
ize
d
fe
a
tu
re
s
we
re
u
se
d
with
n
a
i
v
e
Ba
y
e
s
(NB)
c
las
sifier,
re
fe
rrin
g
to
th
e
stre
n
g
th
o
f
in
c
o
rp
o
ra
ti
n
g
sta
ti
stica
l
a
n
a
ly
si
s in
to
fe
a
tu
re
d
e
sig
n
.
K
ey
w
o
r
d
s
:
Ar
ea
u
n
d
er
th
e
cu
r
v
e
Deo
x
y
r
ib
o
n
u
cleic
ac
id
Ma
ch
in
e
lear
n
in
g
Pro
m
o
ter
Statis
t
ical
f
ea
tu
r
e
an
aly
s
is
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Sin
an
Salim
Mo
h
am
m
ed
Sh
ee
t
T
ec
h
n
ical
Me
d
ical
I
n
s
tr
u
m
en
t
atio
n
,
Po
ly
tech
n
ic
C
o
lleg
e
M
o
s
u
l,
No
r
th
er
n
T
e
ch
n
ical
Un
iv
e
r
s
ity
Mo
s
u
l,
I
r
aq
E
m
ail:
s
in
an
_
s
m
7
6
@
n
tu
.
ed
u
.
i
q
1.
I
NT
RO
D
UCT
I
O
N
R
eg
u
latio
n
o
f
g
en
e
e
x
p
r
ess
io
n
is
a
v
ital
ce
llu
lar
p
r
o
ce
s
s
t
h
at
en
s
u
r
es
d
ev
elo
p
m
e
n
t,
p
h
y
s
io
lo
g
ical
b
alan
ce
,
an
d
a
d
ap
tatio
n
to
e
n
v
ir
o
n
m
en
tal
ch
a
n
g
es.
I
t
d
eter
m
in
es
wh
en
an
d
h
o
w
g
en
es
ar
e
ex
p
r
ess
ed
,
s
h
ap
in
g
p
r
o
tein
d
iv
er
s
ity
an
d
ce
llu
lar
id
en
tity
[
1
]
.
Dy
s
r
eg
u
latio
n
o
f
th
is
p
r
o
ce
s
s
is
clo
s
ely
lin
k
ed
to
h
u
m
an
d
is
ea
s
es
s
u
ch
as
ca
n
ce
r
,
m
etab
o
lic
d
is
o
r
d
er
s
,
an
d
n
eu
r
o
lo
g
ical
co
n
d
itio
n
s
.
P
r
o
m
o
ter
r
eg
io
n
s
wh
ich
ar
esh
o
r
t
d
eo
x
y
r
ib
o
n
u
cleic
ac
id
(
DNA
)
s
tr
etch
es
u
p
s
tr
ea
m
o
f
g
en
est
h
at
ac
t
as
co
n
tr
o
l
h
u
b
s
f
o
r
tr
a
n
s
cr
ip
tio
n
in
itiatio
n
ar
e
am
o
n
g
th
e
cr
itical
r
e
g
u
lato
r
s
[
2
]
.
Pro
m
o
ter
r
eg
io
n
s
p
r
o
v
id
e
d
o
ck
in
g
s
ites
f
o
r
r
ib
o
n
u
cleic
ac
id
(
R
NA
)
p
o
ly
m
er
ase
a
n
d
tr
a
n
s
cr
ip
tio
n
f
ac
to
r
s
.
E
ar
ly
s
tu
d
ies
d
escr
ib
ed
ess
en
tial
m
o
tifs
lik
e
th
e
-
3
5
(
T
T
GACA)
an
d
-
1
0
(
T
AT
AAT
)
elem
en
ts
in
b
ac
ter
ial
p
r
o
m
o
ter
s
,
with
tr
an
s
cr
ip
tio
n
s
tar
tin
g
n
ea
r
a
p
u
r
in
e
d
o
wn
s
tr
ea
m
o
f
th
e
-
1
0
b
o
x
.
Ho
wev
er
,
p
r
o
m
o
ter
s
tr
u
ctu
r
es v
ar
y
wid
ely
ac
r
o
s
s
s
p
ec
ies
[
3
]
.
I
d
en
tify
in
g
p
r
o
m
o
ter
s
r
em
ain
s
ch
allen
g
in
g
b
ec
au
s
e
m
an
y
l
ac
k
co
n
s
er
v
ed
m
o
tifs
an
d
o
v
er
lap
with
o
th
er
r
eg
u
lato
r
y
r
eg
io
n
s
.
T
h
e
ac
cu
r
ate
d
etec
tio
n
is
v
er
y
co
m
p
licated
d
u
e
to
th
eir
s
eq
u
en
ce
v
ar
iab
ilit
y
,
ch
r
o
m
atin
s
tr
u
ctu
r
e,
an
d
s
p
ec
ies
-
s
p
ec
if
ic
d
if
f
er
en
ce
s
.
T
r
ad
i
tio
n
al
co
m
p
u
tatio
n
al
m
eth
o
d
s
,
r
ely
in
g
o
n
m
o
tifs
o
r
p
o
s
itio
n
weig
h
t
m
atr
ices,
o
f
ten
s
u
f
f
er
f
r
o
m
lo
w
ac
cu
r
a
cy
an
d
h
ig
h
f
alse
d
is
co
v
e
r
y
r
ates,
lim
itin
g
th
eir
r
eliab
ilit
y
f
o
r
lar
g
e
-
s
ca
le
g
en
o
m
ic
s
tu
d
ie
[
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
n
o
ve
l m
eth
o
d
fo
r
ex
a
min
in
g
p
r
o
mo
ters
u
s
in
g
s
ta
tis
tica
l a
n
a
lysi
s
a
n
d
…
(
S
in
a
n
S
a
lim Mo
h
a
mme
d
S
h
ee
t
)
4007
T
o
o
v
er
c
o
m
e
th
ese
ch
allen
g
es,
r
esear
ch
er
s
h
av
e
tu
r
n
e
d
to
ar
tific
ial
in
tellig
en
ce
(
AI
)
b
ased
ap
p
r
o
ac
h
es.
Ma
ch
in
e
lear
n
in
g
(
ML
)
an
d
d
ee
p
lear
n
i
n
g
(
DL
)
m
o
d
els
ca
n
ca
p
tu
r
e
b
o
th
s
eq
u
en
ce
lev
el
m
o
tifs
an
d
lo
n
g
-
r
an
g
e
d
e
p
en
d
e
n
cies,
im
p
r
o
v
in
g
p
r
ed
ictio
n
p
e
r
f
o
r
m
an
ce
.
Me
th
o
d
s
s
u
ch
as
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
etwo
r
k
s
(
C
NNs
)
,
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
s
(
R
NNs
)
,
an
d
h
y
b
r
id
C
NN
-
lo
n
g
s
h
o
r
t
-
te
r
m
m
em
o
r
y
(
L
STM
)
m
o
d
els
h
av
e
ac
h
iev
ed
g
o
o
d
r
esu
lts
,
wh
ile
atten
tio
n
m
ec
h
an
is
m
s
an
d
T
r
an
s
f
o
r
m
er
-
b
ased
ar
ch
itectu
r
es
o
f
f
er
n
ew
p
o
s
s
ib
ilit
ies f
o
r
m
o
d
elin
g
p
r
o
m
o
ter
co
m
p
lex
ity
[
5
]
–
[
7
]
.
Desp
ite
th
ese
ad
v
an
ce
s
,
AI
b
ased
m
eth
o
d
s
s
till
f
ac
e
p
r
o
b
le
m
s
o
f
d
ata
s
ca
r
city
an
d
in
ter
p
r
etab
ilit
y
.
L
ar
g
e,
h
ig
h
-
q
u
ality
d
atasets
ar
e
o
f
ten
u
n
av
ailab
le.
T
h
is
s
tu
d
y
aim
s
to
ad
d
r
ess
th
ese
g
ap
s
b
y
d
ev
elo
p
in
g
r
o
b
u
s
t
AI
-
b
ased
p
r
o
m
o
ter
i
d
en
tific
atio
n
m
eth
o
d
s
th
at
in
te
g
r
ate
b
io
lo
g
ical
k
n
o
wled
g
e
with
ML
.
T
h
e
n
ex
t
s
ec
tio
n
r
ev
iews
p
r
e
v
io
u
s
m
e
th
o
d
s
,
with
em
p
h
asis
o
n
th
e
ev
o
lu
tio
n
f
r
o
m
t
r
ad
itio
n
al
m
o
d
els
to
m
o
d
er
n
AI
-
d
r
iv
en
ap
p
r
o
ac
h
es.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
B
ec
au
s
e
th
e
m
eth
o
d
s
wh
ich
d
ep
en
d
o
n
tr
a
d
itio
n
al
lab
o
r
ato
r
y
ar
e
o
f
ten
r
eso
u
r
ce
-
i
n
ten
s
iv
e,
s
lo
w,
an
d
n
o
t
s
ca
lab
le
f
o
r
wh
o
le
-
g
en
o
m
e
s
tu
d
ies,
co
m
p
u
tatio
n
al
ap
p
r
o
ac
h
es
h
av
e
b
ec
o
m
e
a
v
er
y
im
p
o
r
tan
t
to
o
ls
in
th
e
p
r
ed
ictio
n
o
f
p
r
o
m
o
to
[
8
]
.
M
L
tech
n
iq
u
es
am
o
n
g
th
ese
co
m
p
u
tatio
n
al
ap
p
r
o
ac
h
es
h
a
v
e
b
ee
n
em
er
g
e
d
d
u
e
t
o
th
eir
p
ar
ticu
la
r
ly
e
f
f
ec
tiv
e,
ca
p
ab
le
o
f
u
n
co
v
er
in
g
in
tr
icate
s
eq
u
en
ce
p
atter
n
s
an
d
d
ep
en
d
en
cies
th
at
m
ig
h
t
b
e
o
v
er
lo
o
k
ed
b
y
co
n
v
en
tio
n
al
al
g
o
r
ith
m
s
.
ML
m
o
d
els
ca
n
ac
c
u
r
ately
d
is
tin
g
u
is
h
b
etwe
en
p
r
o
m
o
ter
r
eg
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n
s
an
d
b
ac
k
g
r
o
u
n
d
g
e
n
o
m
ic
s
eq
u
en
ce
s
with
h
ig
h
p
r
ed
ictiv
e
p
o
wer
b
y
co
n
v
er
tin
g
r
aw
DNA
s
eq
u
en
ce
s
in
to
s
tr
u
ctu
r
ed
f
ea
tu
r
e
r
ep
r
esen
tatio
n
s
.
A
m
i
n
e
t
a
l
.
[
9
]
p
r
o
p
o
s
e
d
a
s
tu
d
y
u
s
i
n
g
a
DL
-
b
a
s
e
d
a
p
p
r
o
a
c
h
i
n
i
d
e
n
ti
f
i
c
a
ti
o
n
a
n
d
c
la
s
s
if
i
c
a
t
i
o
n
o
f
b
a
c
t
e
r
i
a
l
s
i
g
m
a
p
r
o
m
o
t
e
r
s
u
s
in
g
b
r
a
n
c
h
e
d
C
N
N
s
.
T
h
e
i
r
m
e
t
h
o
d
w
h
i
c
h
i
s
c
a
l
l
e
d
p
r
o
m
p
t
-
le
a
r
n
i
n
g
p
r
e
-
t
r
a
i
n
e
d
l
a
n
g
u
a
g
e
m
o
d
e
l
f
o
r
p
r
o
m
o
t
e
r
p
r
e
d
i
c
t
i
o
n
(
PL
P
M
p
r
o
)
,
h
as
b
ee
n
d
e
s
i
g
n
e
d
t
o
d
is
ti
n
g
u
i
s
h
b
etw
e
e
n
p
r
o
m
o
t
e
r
a
n
d
non
-
p
r
o
m
o
t
e
r
s
e
q
u
e
n
c
e
s
i
n
a
d
d
i
t
i
o
n
t
o
p
r
o
m
o
t
e
r
s
'
c
l
as
s
i
f
i
c
ati
o
n
i
n
t
o
d
i
f
f
e
r
e
n
t
s
i
g
m
a
f
a
c
t
o
r
c
a
t
e
g
o
r
i
e
s
,
s
u
c
h
a
s
σ
⁷
⁰
a
n
d
σ
³
²
.
T
h
e
s
y
s
t
e
m
u
s
e
d
p
a
r
a
l
l
e
l
c
o
n
v
o
l
u
t
i
o
n
al
b
r
a
n
c
h
e
s
t
o
e
x
t
r
a
ct
d
i
v
e
r
s
e
f
e
at
u
r
e
r
ep
r
e
s
e
n
t
a
ti
o
n
s
f
r
o
m
D
N
A
s
e
q
u
e
n
c
e
s
,
a
n
d
t
h
i
s
r
es
u
l
t
i
n
a
n
i
m
p
r
o
v
e
m
e
n
t
i
n
th
e
c
l
a
s
s
i
f
ic
a
t
i
o
n
p
e
r
f
o
r
m
a
n
c
e
.
T
h
e
i
r
C
NN
-
b
a
s
e
d
f
r
a
m
e
w
o
r
k
a
c
h
i
e
v
e
d
a
a
c
c
u
r
a
cy
a
n
d
g
e
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e
r
a
l
i
z
a
b
i
l
it
y
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n
b
o
t
h
b
i
n
a
r
y
a
n
d
m
u
l
t
i
c
l
ass
p
r
o
m
o
t
e
r
p
r
e
d
i
c
t
i
o
n
t
as
k
s
[
9
]
.
T
ay
ar
a
et
a
l.
[
1
0
]
in
th
e
s
am
e
y
ea
r
,
in
t
r
o
d
u
ce
d
a
h
y
b
r
id
d
ee
p
-
lear
n
in
g
f
r
am
ew
o
r
k
ca
lled
id
en
tific
atio
n
o
f
p
r
o
k
a
r
y
o
tic
p
r
o
m
o
te
r
s
an
d
th
eir
s
tr
en
g
th
v
ia
win
d
o
ws
(
iPSW
)
u
s
in
g
p
s
eu
d
o
d
in
u
cleo
tid
e
co
m
p
o
s
itio
n
(
Ps
eDN
C
)
-
b
ased
d
ee
p
lear
n
in
g
to
b
e
u
s
ed
in
th
e
id
e
n
tific
atio
n
o
f
p
r
o
k
ar
y
o
tic
p
r
o
m
o
ter
s
an
d
class
if
y
th
em
in
to
two
ca
teg
o
r
ies,
s
tr
o
n
g
an
d
wea
k
.
T
h
e
s
t
u
d
y
in
te
g
r
ates
b
etwe
en
C
NNs
an
d
Ps
eDN
C
.
T
h
is
h
y
b
r
id
ar
c
h
itectu
r
e
h
as
b
ee
n
ap
p
lied
o
n
b
en
ch
m
ar
k
E
co
li
d
atasets
an
d
s
h
o
wed
h
ig
h
ac
cu
r
ac
y
i
n
p
r
o
m
o
ter
d
etec
tio
n
[
1
0
]
.
Mo
r
ae
s
et
a
l.
[
1
1
]
p
r
o
p
o
s
ed
C
ap
s
Pro
m
,
wh
ich
is
a
ca
p
s
u
le
n
etwo
r
k
–
b
ased
m
o
d
el
u
s
ed
to
id
en
tify
p
r
o
m
o
te
r
ac
r
o
s
s
s
ev
en
d
if
f
er
e
n
t
o
r
g
an
is
m
s
,
in
clu
d
in
g
eu
k
a
r
y
o
tes
an
d
p
r
o
k
ar
y
o
tes.
C
ap
s
Pro
m
g
et
b
e
n
if
it
f
r
o
m
th
e
ab
ilit
y
o
f
th
e
ca
p
s
u
le
n
e
two
r
k
’
t
o
m
ai
n
tain
h
ier
ar
ch
i
ca
l
r
elatio
n
s
h
ip
s
with
in
s
eq
u
en
ce
p
atter
n
s
.
T
h
is
m
eth
o
d
d
em
o
n
s
tr
ated
co
m
p
e
titi
v
e
F1
-
s
co
r
es
s
u
r
p
ass
in
g
b
aselin
e
C
NN
ap
p
r
o
ac
h
es
i
n
f
iv
e
o
u
t
o
f
s
ev
en
d
atasets
.
T
h
e
au
th
o
r
s
em
p
h
asized
th
e
g
en
e
r
aliza
b
ilit
y
o
f
th
e
C
ap
s
Pro
m
’
s
g
s
y
s
tem
,
ac
co
r
d
i
n
g
to
its
s
tr
en
g
th
in
cr
o
s
s
-
s
p
ec
ies p
r
o
m
o
ter
p
r
ed
ict
io
n
an
d
p
o
ten
tial f
o
r
tr
an
s
f
e
r
l
ea
r
n
in
g
(
T
L
)
ac
r
o
s
s
g
en
o
m
ic
c
o
n
tex
ts
[
1
1
]
.
Z
h
an
g
et
a
l
.
[
1
2
]
i
n
tr
o
d
u
ce
d
a
m
o
d
el
f
o
r
p
r
o
m
o
ter
p
r
ed
ict
io
n
.
T
h
is
m
o
d
el
p
r
o
d
u
ce
s
a
h
y
b
r
id
DL
f
r
am
ewo
r
k
co
m
b
in
in
g
C
NNs,
ca
p
s
u
le
n
etwo
r
k
s
,
b
id
ir
ec
tio
n
al
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
B
i
-
L
STM
)
,
an
d
a
s
elf
-
atten
tio
n
m
ec
h
a
n
is
m
to
id
en
tify
p
r
o
m
o
ter
s
ef
f
ec
tiv
ely
an
d
class
if
y
th
eir
s
tr
en
g
th
.
I
t
u
s
es
o
n
e
-
h
o
t
en
co
d
in
g
to
r
ep
r
esen
t
DNA
s
eq
u
en
ce
s
an
d
g
ets
b
en
if
its
f
r
o
m
b
o
th
lo
ca
l
an
d
g
lo
b
al
s
eq
u
en
ce
f
ea
tu
r
es
to
en
h
an
ce
p
r
ed
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n
p
er
f
o
r
m
an
ce
.
T
h
e
m
o
d
el
h
as
ac
h
iev
ed
an
ac
cu
r
ac
y
o
f
a
p
p
r
o
x
im
ately
8
6
%
f
o
r
p
r
o
m
o
ter
id
en
tific
atio
n
an
d
ar
o
u
n
d
7
3
.
5
% f
o
r
p
r
o
m
o
ter
s
tr
en
g
th
class
if
icatio
n
[
1
2
]
.
I
n
an
o
th
er
r
elate
d
s
tu
d
y
,
L
i
et
a
l.
[
1
3
]
d
ev
elo
p
ed
a
n
o
v
el
ap
p
r
o
c
h
PLPMp
r
o
.
T
h
is
ap
p
r
o
ac
h
en
h
an
ce
d
th
e
p
r
ed
ictio
n
o
f
th
e
p
r
o
m
o
to
r
s
eq
u
en
ce
b
y
c
o
m
b
in
in
g
th
e
p
r
o
m
p
t
-
lear
n
i
n
g
with
p
r
e
-
tr
ain
ed
lan
g
u
ag
e
m
o
d
els.
T
h
eir
s
tu
d
y
u
s
ed
p
r
o
m
p
t
-
b
ased
f
in
e
-
tu
n
in
g
to
lev
er
a
g
e
g
en
o
m
ic
r
ep
r
esen
tatio
n
s
lear
n
ed
f
r
o
m
lar
g
e
-
s
ca
le
tr
ain
in
g
co
r
p
o
r
a,
wh
ich
in
cr
ea
s
e
th
e
ab
il
ity
o
f
th
e
s
y
s
tem
to
ca
p
tu
r
e
co
m
p
lex
p
r
o
m
o
ter
s
eq
u
en
ce
f
ea
t
u
r
es
m
o
r
e
e
f
f
ec
t
iv
ely
.
Af
ter
ev
alu
ate
d
th
e
s
y
s
tem
o
n
b
e
n
ch
m
ar
k
d
atasets
f
r
o
m
th
e
E
u
k
ar
y
o
tic
p
r
o
m
o
te
r
d
ata
b
ase
,
th
e
r
esu
lts
ac
h
iev
ed
i
n
b
o
th
p
r
ec
is
io
n
an
d
r
ec
all
d
e
m
o
n
s
tr
ated
n
o
t
ab
le
im
p
r
o
v
em
en
ts
co
m
p
ar
in
g
to
co
n
v
en
tio
n
al
tr
an
s
f
o
r
m
er
-
b
ased
m
o
d
els
s
u
ch
as
DNA
b
id
ir
ec
tio
n
al
en
co
d
er
r
ep
r
esen
tatio
n
s
f
r
o
m
tr
a
n
s
f
o
r
m
er
s
(
B
E
R
T
)
[
1
3
]
.
Pau
l
et
a
l.
[
1
4
]
d
ev
elo
p
ed
m
ac
h
in
e
lear
n
in
g
an
d
d
u
p
lex
s
tab
ilit
y
p
r
o
m
o
ter
p
r
ed
ictio
n
(
ML
DSPP
)
n
am
ed
s
y
s
tem
f
o
c
u
s
in
g
o
n
b
a
cter
ial
g
en
o
m
es.
T
h
is
s
tu
d
y
is
a
to
o
l
d
esig
n
e
d
to
d
etec
t
p
r
o
m
o
to
r
r
e
g
io
n
s
cr
o
s
s
1
2
p
r
o
k
ar
y
o
tic
s
p
ec
ies.
T
h
is
m
eth
o
d
u
s
ed
ML
alg
o
r
ith
m
s
s
u
ch
as
ex
tr
e
m
e
g
r
a
d
ien
t
b
o
o
s
t
in
g
(
XGBo
o
s
t)
with
s
tr
u
ctu
r
al
DNA
f
ea
tu
r
es
s
u
ch
as
d
u
p
lex
s
tab
ilit
y
.
T
h
e
r
esu
lt
s
o
b
tain
ed
f
r
o
m
u
s
in
g
ML
DS
PP
d
em
o
n
s
tr
ated
a
s
u
p
er
io
r
ity
to
ex
is
tin
g
to
o
ls
lik
e
Sig
m
a7
0
p
r
ed
an
d
iPro
m
o
t
er
2
L
,
wh
ic
h
ac
h
iev
e
d
F1
-
s
co
r
es
ab
o
v
e
th
a
n
9
5
%.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
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2
5
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3
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1
4
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Ash
ay
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et
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1
5
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p
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tech
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m
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[
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id
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[
1
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]
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ac
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S
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l
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L
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k
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a
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e
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D
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a
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d
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a
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B
a
y
es
(
N
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h
es
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c
l
ass
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f
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s
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e
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e
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e
ct
e
d
f
o
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t
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e
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r
c
o
m
p
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m
e
n
t
a
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y
s
t
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g
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h
s
:
i
)
SV
M
h
a
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d
l
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s
h
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d
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at
a
,
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f
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p
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a
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a
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g
,
ii
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)
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N
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c
a
p
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r
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s
l
o
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al
s
e
q
u
e
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c
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s
i
m
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a
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i
ti
e
s
,
i
v
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f
f
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ct
i
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ly
m
a
n
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g
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t
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i
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n
s
,
a
n
d
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NB
p
e
r
f
o
r
m
s
w
e
ll
u
n
d
e
r
p
r
o
b
a
b
i
l
i
s
ti
c
a
s
s
u
m
p
ti
o
n
s
.
T
h
is
d
i
v
e
r
s
e
c
l
as
s
i
f
i
e
r
s
el
e
c
ti
o
n
e
n
s
u
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s
a
c
o
m
p
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h
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s
i
v
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v
a
l
u
at
i
o
n
o
f
t
h
e
p
r
o
p
o
s
e
d
f
e
a
t
u
r
e
s
.
3.
M
E
T
H
O
DO
L
O
G
Y
T
h
is
s
ec
tio
n
illu
s
tr
ates
th
e
o
v
er
all
m
eth
o
d
o
lo
g
y
u
s
ed
in
t
h
is
s
tu
d
y
.
I
t
s
tar
ts
f
r
o
m
d
ata
p
r
e
p
r
o
ce
s
s
in
g
an
d
f
ea
tu
r
e
ex
tr
ac
tio
n
m
eth
o
d
s
to
th
e
m
o
d
el
d
ev
elo
p
m
en
t
a
n
d
p
er
f
o
r
m
an
ce
ev
alu
ati
o
n
.
Fig
u
r
e
1
s
h
o
ws
th
e
wo
r
k
f
lo
w
in
t
h
is
s
tu
d
y
.
Fig
u
r
e
1
.
Flo
wch
ar
t
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Ar
tif
I
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tell
I
SS
N:
2252
-
8
9
3
8
A
n
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l m
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fo
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4009
3
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1
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ataset
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I
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v
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(
UC
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ML
r
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o
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ito
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y
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1
8
]
,
it
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f
1
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n
u
cleo
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eq
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ase
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3
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2
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e
a
t
ure
eng
ineering
3
.
2
.
1
.
B
a
s
ic
f
ea
t
ure
eng
ineering
B
asic
f
ea
tu
r
e
en
g
in
ee
r
in
g
m
eth
o
d
an
al
y
s
es
DNA
s
eq
u
en
ce
s
b
ased
o
n
th
e
c
o
m
p
o
n
en
t
s
o
f
th
eir
f
u
n
d
am
e
n
tal
n
u
cleo
tid
e
-
ad
en
i
n
e
(
A)
,
th
y
m
in
e
(
T
)
,
cy
t
o
s
in
e
(
C
)
,
an
d
g
u
an
in
e
(
G)
.
E
ac
h
DNA
s
eq
u
en
ce
was
b
r
o
k
e
n
d
o
wn
in
t
o
in
d
i
v
id
u
al
n
u
cleo
tid
es,
an
d
ea
ch
n
u
cle
o
tid
e
r
ef
er
r
ed
t
o
as
a
s
ep
a
r
ate
f
e
atu
r
e.
T
h
is
m
et
h
o
d
ca
n
id
en
tify
s
h
o
r
t,
lo
ca
lized
n
u
cleo
tid
e
p
atter
n
s
wh
ich
ar
e
im
p
o
r
tan
t
in
d
is
tin
g
u
is
h
in
g
b
et
wee
n
PS
an
d
n
_
PS
ty
p
es.
3
.
2
.
2
.
Dev
elo
ped f
ea
t
ure
eng
i
neer
ing
T
h
e
aim
o
f
d
ev
elo
p
e
d
f
ea
tu
r
e
en
g
in
ee
r
in
g
ap
p
r
o
ac
h
is
to
e
n
h
an
ce
th
e
ac
cu
r
ac
y
o
f
class
if
icatio
n
b
y
ex
tr
ac
tin
g
a
co
m
p
r
eh
e
n
s
iv
e
s
et
o
f
b
io
lo
g
ically
m
ea
n
in
g
f
u
l
attr
ib
u
tes
f
r
o
m
DNA
s
eq
u
e
n
ce
s
.
T
h
is
m
eth
o
d
in
teg
r
ates
d
if
f
er
e
n
t
ev
al
u
atio
n
in
o
r
d
e
r
to
ca
p
tu
r
e
b
o
t
h
g
lo
b
al
an
d
lo
ca
l
s
eq
u
en
ce
c
h
ar
ac
ter
is
tics
,
th
ese
ev
alu
atio
n
s
ar
e
n
u
cle
o
tid
e
co
m
p
o
s
itio
n
an
aly
s
is
,
GC
co
n
ten
t
m
ea
s
u
r
em
en
t,
k
-
m
er
f
r
eq
u
en
cy
p
r
o
f
ilin
g
,
a
n
d
s
eq
u
en
ce
co
m
p
le
x
ity
ev
alu
at
io
n
.
Nu
cleo
tid
e
co
u
n
tin
g
d
e
ter
m
in
e
th
e
o
cc
u
r
r
en
ce
s
o
f
ad
en
in
e,
th
y
m
in
e,
cy
to
s
in
e,
an
d
g
u
an
in
e.
GC
co
n
ten
t
an
aly
s
is
m
ea
s
u
r
es
f
r
eq
u
en
cy
o
f
g
u
an
in
e
an
d
cy
to
s
in
e
n
u
cleo
tid
es
wh
ich
is
im
p
o
r
tan
t
in
DNA
s
tab
ilit
y
a
cc
o
r
d
in
g
to
th
eir
tr
ip
le
h
y
d
r
o
g
en
b
o
n
d
s
.
K
-
m
er
an
aly
s
is
in
v
esti
g
ates
r
ec
u
r
r
in
g
n
u
cleo
tid
e
m
o
tifs
o
f
len
g
th
f
i
n
ally
,
s
eq
u
en
ce
co
m
p
lex
ity
a
n
aly
s
is
ass
es
s
es
th
e
v
ar
iab
ilit
y
an
d
ir
r
eg
u
lar
ity
in
n
u
cleo
tid
e
d
is
tr
ib
u
tio
n
.
T
ab
le
1
illu
s
tr
ates th
e
s
ig
n
if
ican
t c
o
m
p
o
s
itio
n
al
an
d
s
tr
u
ctu
r
al
d
if
f
er
en
ce
s
b
etwe
en
PS
an
d
n
_
PS
b
y
u
s
in
g
De
v
elo
p
ed
Featu
r
es.
3
.
3
.
F
e
a
t
ure
s
t
a
t
is
t
ics a
nd
s
ig
nifica
nce
bio
lo
g
ica
l per
f
o
r
m
a
nce
m
e
t
rics
D
i
f
f
e
r
e
n
t
s
t
a
t
i
s
t
i
c
a
l
a
n
d
e
v
a
l
u
a
t
i
o
n
m
e
t
r
i
c
s
h
a
v
e
b
e
e
n
u
s
e
d
,
i
n
o
r
d
e
r
t
o
a
s
s
e
s
s
t
h
e
s
i
g
n
i
f
i
c
a
n
c
e
a
n
d
p
e
r
f
o
r
m
a
n
c
e
o
f
e
a
c
h
f
e
a
t
u
r
e
i
n
t
h
e
c
l
a
s
s
i
f
i
c
a
t
i
o
n
t
a
s
k
.
T
h
e
s
e
m
e
t
r
i
c
s
g
i
v
e
a
c
c
u
r
a
t
e
a
n
a
l
y
s
i
s
f
o
r
f
e
a
t
u
r
e
d
i
s
t
r
i
b
u
t
i
o
n
s
a
n
d
t
h
e
i
r
r
e
l
a
t
i
o
n
s
h
i
p
s
w
i
t
h
t
h
e
c
l
a
s
s
i
f
i
c
a
t
i
o
n
r
e
s
u
l
t
s
.
T
h
e
m
e
t
r
i
c
s
u
s
e
d
i
n
t
h
i
s
s
t
u
d
y
w
e
r
e
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
f
i
c
i
e
n
t
s
,
r
o
o
t
m
e
a
n
s
q
u
a
r
e
e
r
r
o
r
(
R
M
S
E
)
,
m
e
a
n
a
n
d
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
(
S
D
)
,
s
i
g
n
a
l
-
to
-
n
o
i
s
e
r
a
t
i
o
(
S
N
R
)
,
a
n
d
t
h
e
a
r
e
a
u
n
d
e
r
t
h
e
c
u
r
v
e
(
A
U
C
)
.
T
h
e
f
o
r
m
u
l
a
s
f
o
r
t
h
e
s
e
m
e
t
r
i
c
s
a
r
e
d
e
t
a
i
l
e
d
a
s
f
o
l
l
o
w
s
[
1
9
]
,
[
2
0
]
.
i)
C
o
r
r
elatio
n
:
th
e
co
r
r
elatio
n
co
ef
f
icien
t
m
ea
s
u
r
in
g
th
e
r
el
atio
n
s
h
ip
b
etwe
en
ea
ch
f
ea
t
u
r
e
x
an
d
th
e
class
if
icatio
n
tar
g
et
y
.
I
n
(
1
)
s
h
o
ws th
e
m
ath
em
atica
l f
o
r
m
u
l
a
o
f
co
r
r
elatio
n
:
(
,
)
=
∑
(
−
̅
)
(
−
̅
)
=
1
√
∑
(
−
̅
)
2
=
1
√
∑
(
−
̅
)
2
=
1
(
1
)
w
h
er
e
,
ar
e
th
e
v
alu
es
o
f
f
ea
tu
r
e
an
d
tar
g
et
f
o
r
s
am
p
le
i
,
̅
,
̅
ar
e
th
e
m
ea
n
s
o
f
X
a
n
d
Y
,
n
is
th
e
n
u
m
b
er
o
f
s
am
p
les.
ii)
R
o
o
t m
ea
n
s
q
u
ar
e
(
R
MS)
: R
MS
is
u
s
ed
to
ass
es
s
th
e
av
er
ag
e
m
ag
n
itu
d
e
o
f
a
f
ea
tu
r
e
as (
2
)
.
(
)
=
√
1
∑
2
=
1
(
2
)
iii)
Me
an
an
d
SD
:
th
e
m
ea
n
a
n
d
SD
o
f
a
f
ea
tu
r
e
d
escr
ib
e
its
ce
n
tr
al
ten
d
e
n
cy
a
n
d
v
ar
iab
ilit
y
is
s
h
o
wn
in
(
3
)
an
d
(
4
)
:
(
)
=
1
∑
=
1
(
3
)
(
)
=
√
1
∑
(
=
1
−
̅
)
2
(
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
5
,
Octo
b
er
2
0
2
5
:
4
0
0
6
-
4
0
1
6
4010
iv
)
SNR
: S
NR
q
u
an
tifie
s
h
o
w
m
u
ch
s
ig
n
al
is
p
r
esen
t in
a
f
ea
t
u
r
e
r
elativ
e
to
its
n
o
is
e
as in
(
5
)
:
(
)
=
̅
(
)
(
5
)
v)
AUC:
to
d
eter
m
in
e
th
e
co
n
t
r
ib
u
tio
n
o
f
ea
c
h
f
ea
tu
r
e
i
n
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
m
o
d
el,
a
n
ab
latio
n
s
tu
d
y
was
p
er
f
o
r
m
ed
.
E
ac
h
f
ea
tu
r
e
h
as
b
ee
n
r
em
o
v
ed
in
d
iv
i
d
u
all
y
,
an
d
t
h
e
class
if
ier
h
as
b
ee
n
r
etr
ain
ed
an
d
th
e
AUC
o
f
th
e
m
o
d
el
with
o
u
t
th
is
f
ea
tu
r
e
was
r
ec
o
r
d
ed
.
I
n
(
6
)
u
s
ed
to
d
eter
m
in
e
AUC
d
if
f
er
en
ce
d
u
e
to
r
em
o
v
al
o
f
f
ea
t
u
r
e
is
:
∆
=
−
−
(
6
)
wh
er
e,
is
th
e
m
o
d
el
p
e
r
f
o
r
m
an
ce
with
all
f
ea
tu
r
es,
−
is
th
e
p
er
f
o
r
m
an
ce
af
te
r
r
em
o
v
in
g
f
ea
tu
r
e
.
T
ab
le
1
.
Su
m
m
a
r
y
o
f
d
e
v
elo
p
ed
f
ea
tu
r
es f
o
r
PS
an
d
n
_
PS
F
e
a
t
u
r
e
N
u
c
l
e
o
t
i
d
e
P
S
a
v
e
r
a
g
e
v
a
l
u
e
n
_
P
S
a
v
e
r
a
g
e
v
a
l
u
e
B
i
o
l
o
g
i
c
a
l
si
g
n
i
f
i
c
a
n
c
e
N
u
c
l
e
o
t
i
d
e
c
o
u
n
t
A
d
e
n
i
n
e
(
A
)
1
5
.
7
9
1
4
.
0
2
A
a
p
p
e
a
r
s
m
o
r
e
o
f
t
e
n
i
n
P
S
r
e
g
i
o
n
s,
p
l
a
y
i
n
g
a
r
o
l
e
i
n
f
a
c
i
l
i
t
a
t
i
n
g
D
N
A
st
r
a
n
d
s
e
p
a
r
a
t
i
o
n
a
n
d
i
n
i
t
i
a
t
i
n
g
t
r
a
n
s
c
r
i
p
t
i
o
n
.
Th
y
mi
n
e
(
T)
1
7
.
1
9
1
5
.
1
1
A
h
i
g
h
p
r
e
s
e
n
c
e
o
f
T
i
n
P
S
r
e
g
i
o
n
s
e
n
h
a
n
c
e
s
D
N
A
f
l
e
x
i
b
i
l
i
t
y
,
m
a
k
i
n
g
i
t
e
a
si
e
r
t
o
u
n
w
i
n
d
t
h
e
st
r
a
n
d
s
d
u
r
i
n
g
t
r
a
n
s
c
r
i
p
t
i
o
n
.
C
y
t
o
s
i
n
e
(
C
)
1
2
.
6
2
1
3
.
5
1
A
l
o
w
c
o
u
n
t
o
f
C
c
o
n
t
e
n
t
i
n
P
S
r
e
g
i
o
n
s
r
e
su
l
t
s
i
n
d
i
mi
n
i
s
h
e
d
st
r
u
c
t
u
r
a
l
st
a
b
i
l
i
t
y
o
f
t
h
e
D
N
A
.
G
u
a
n
i
n
e
(
G
)
1
1
.
4
1
4
.
4
5
A
d
e
c
r
e
a
se
d
l
e
v
e
l
o
f
G
i
n
P
S
r
e
g
i
o
n
s
e
n
h
a
n
c
e
s
a
c
c
e
ss
i
b
i
l
i
t
y
f
o
r
t
h
e
t
r
a
n
s
c
r
i
p
t
i
o
n
m
a
c
h
i
n
e
r
y
.
N
u
c
l
e
o
t
i
d
e
c
o
u
n
t
r
a
n
g
e
(
p
e
r
5
7
n
u
c
l
e
o
t
i
d
e
s)
A
d
e
n
i
n
e
(
A
)
15
–
18
13
–
15
I
n
P
S
r
e
g
i
o
n
s
,
h
i
g
h
A
c
o
n
t
e
n
t
a
i
d
s
i
n
D
N
A
u
n
w
i
n
d
i
n
g
,
w
h
e
r
e
a
s
n
_
P
S
r
e
g
i
o
n
s
d
i
s
p
l
a
y
a
mo
r
e
b
a
l
a
n
c
e
d
n
u
c
l
e
o
t
i
d
e
c
o
m
p
o
si
t
i
o
n
.
Th
y
mi
n
e
(
T)
16
–
19
14
–
16
I
n
c
r
e
a
se
d
T
l
e
v
e
l
s
i
n
P
S
r
e
g
i
o
n
s
c
o
n
t
r
i
b
u
t
e
t
o
g
r
e
a
t
e
r
D
N
A
f
l
e
x
i
b
i
l
i
t
y
,
w
h
i
l
e
n
_
P
S
r
e
g
i
o
n
s
p
r
e
s
e
r
v
e
st
r
u
c
t
u
r
a
l
st
a
b
i
l
i
t
y
.
C
y
t
o
s
i
n
e
(
C
)
11
–
13
13
–
14
A
d
e
c
l
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n
e
i
n
C
c
o
n
t
e
n
t
w
i
t
h
i
n
P
S
a
r
e
a
s
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a
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s
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o
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e
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u
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e
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D
N
A
s
t
a
b
i
l
i
t
y
,
f
a
c
i
l
i
t
a
t
i
n
g
t
r
a
n
scr
i
p
t
i
o
n
.
G
u
a
n
i
n
e
(
G
)
10
–
12
14
–
15
Le
ss
G
i
n
P
S
r
e
g
i
o
n
s
i
m
p
r
o
v
e
s
a
c
c
e
ss
f
o
r
t
r
a
n
scr
i
p
t
i
o
n
f
a
c
t
o
r
s.
G
C
c
o
n
t
e
n
t
(
%)
—
40
–
4
5
%
48
–
5
2
%
A
l
o
w
e
r
G
C
c
o
n
t
e
n
t
i
n
P
S
e
n
h
a
n
c
e
s
D
N
A
f
l
e
x
i
b
i
l
i
t
y
,
w
h
e
r
e
a
s
h
i
g
h
e
r
G
C
c
o
n
t
e
n
t
i
n
n
_
P
S
s
t
r
e
n
g
t
h
e
n
s
D
N
A
st
r
u
c
t
u
r
e
.
K
-
mer
a
n
a
l
y
si
s
—
C
o
mm
o
n
m
o
t
i
f
s s
u
c
h
a
s
TA
TA
,
C
G
G
,
a
n
d
G
C
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o
c
c
u
r
f
r
e
q
u
e
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t
l
y
,
i
n
d
i
c
a
t
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n
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a
r
i
c
h
p
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se
n
c
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o
f
r
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g
u
l
a
t
o
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y
s
e
q
u
e
n
c
e
s
I
r
r
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g
u
l
a
r
o
r
l
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se
l
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o
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d
p
a
t
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n
s
w
i
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r
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p
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o
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m
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n
P
S
r
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i
o
n
s
h
e
l
p
c
o
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t
r
o
l
g
e
n
e
e
x
p
r
e
ssi
o
n
;
su
c
h
mo
t
i
f
s
a
r
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g
e
n
e
r
a
l
l
y
a
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i
o
n
s
.
S
e
q
u
e
n
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o
m
p
l
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t
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—
El
e
v
a
t
e
d
c
o
m
p
l
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x
i
t
y
w
i
t
h
d
i
v
e
r
se
m
o
t
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f
s a
n
d
st
r
u
c
t
u
r
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me
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t
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Li
mi
t
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c
h
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a
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d
b
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b
a
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c
a
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d
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p
e
t
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e
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c
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s
Th
e
g
r
e
a
t
e
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se
q
u
e
n
c
e
c
o
m
p
l
e
x
i
t
y
f
o
u
n
d
i
n
P
S
r
e
f
e
r
s
t
o
t
h
e
p
r
e
s
e
n
c
e
o
f
r
e
g
u
l
a
t
o
r
y
e
l
e
m
e
n
t
s,
w
h
i
l
e
t
h
e
l
o
w
e
r
c
o
m
p
l
e
x
i
t
y
i
n
n
_
P
S
i
mp
l
i
e
s
m
i
n
i
m
a
l
r
e
g
u
l
a
t
o
r
y
f
u
n
c
t
i
o
n
.
3
.
4
.
Cla
s
s
if
ier
ini
t
ia
liza
t
io
n
a
nd
m
o
del selec
t
io
n
3
.
4
.
1
.
Su
pp
o
rt
v
ec
t
o
r
m
a
chi
ne
SVM
is
an
ef
f
ec
tiv
e
class
if
ier
f
o
r
h
an
d
lin
g
c
o
m
p
lex
,
h
ig
h
-
d
im
en
s
io
n
al
d
ata
b
y
m
ax
i
m
izin
g
th
e
m
ar
g
in
b
etwe
en
class
es u
s
in
g
k
er
n
el
f
u
n
ctio
n
s
[
2
1
]
.
A
lin
ea
r
k
er
n
el
was d
eter
m
in
e
d
u
s
in
g
(
7
)
[
2
2
]
:
(
)
=
∑
(
.
)
+
=
1
(
7
)
w
h
er
e
is
th
e
L
ag
r
an
g
e
m
u
ltip
lier
,
class
lab
els,
an
d
s
u
p
p
o
r
t v
ec
to
r
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
n
o
ve
l m
eth
o
d
fo
r
ex
a
min
in
g
p
r
o
mo
ters
u
s
in
g
s
ta
tis
tica
l a
n
a
lysi
s
a
n
d
…
(
S
in
a
n
S
a
lim Mo
h
a
mme
d
S
h
ee
t
)
4011
3
.
4
.
2
.
K
-
nea
re
s
t
neig
hb
o
rs
KNN
is
a
n
o
n
-
p
ar
am
etr
ic,
in
s
tan
ce
-
b
ased
lear
n
in
g
alg
o
r
ith
m
th
at
class
if
ie
s
a
s
am
p
le
b
ased
o
n
th
e
m
ajo
r
ity
lab
el
am
o
n
g
its
k
clo
s
est n
eig
h
b
o
r
s
in
th
e
f
ea
tu
r
e
s
p
ac
e
[
2
3
]
,
as in
(
8
)
:
=
a
r
g
∑
(
=
)
=
1
(
8
)
w
h
er
e
(
=
)
r
ep
r
esen
t
th
e
i
n
d
icato
r
f
u
n
ctio
n
,
if
(
=
)
th
e
v
al
u
e
is
1
a
n
d
o
th
er
wis
e
0
.
k
is
s
ev
er
al
n
ea
r
est n
eig
h
b
o
r
s
.
3
.
4
.
3
.
L
o
g
is
t
ic
re
g
re
s
s
io
n
L
R
is
a
wid
ely
-
u
s
ed
lin
ea
r
m
o
d
el
th
at
esti
m
ates
th
e
p
r
o
b
ab
ilit
y
o
f
class
m
em
b
e
r
s
h
ip
th
r
o
u
g
h
a
lo
g
is
tic
f
u
n
ctio
n
.
I
ts
s
im
p
licity
allo
ws
f
o
r
s
tr
aig
h
tf
o
r
war
d
in
ter
p
r
etatio
n
o
f
f
ea
tu
r
e
c
o
n
tr
i
b
u
tio
n
s
v
ia
m
o
d
el
co
ef
f
icien
ts
[
2
4
]
.
T
h
e
m
ath
em
atica
l f
o
r
m
u
la
s
h
o
wn
in
(
9
)
:
(
=
1
\
)
=
1
1
+
−
(
.
+
)
(
9
)
w
h
er
e
is
f
ea
tu
r
e
v
ec
to
r
,
w
r
ep
r
esen
ts
th
e
weig
h
t v
ec
to
r
,
an
d
b
is
th
e
b
ias ter
m
.
3
.
4
.
4
.
Na
iv
e
B
a
y
es
NB
clas
s
if
ier
s
r
ely
o
n
s
tr
o
n
g
co
n
d
itio
n
al
in
d
e
p
en
d
e
n
ce
ass
u
m
p
tio
n
s
b
etwe
en
f
ea
tu
r
es
to
co
m
p
u
t
e
p
o
s
ter
io
r
p
r
o
b
a
b
ilit
ies
ef
f
icien
tly
.
Desp
ite
its
s
im
p
licity
,
NB
p
er
f
o
r
m
s
s
u
r
p
r
is
in
g
ly
well
i
n
h
ig
h
-
d
im
en
s
io
n
al
s
p
ac
es
an
d
is
p
ar
ticu
la
r
ly
ef
f
e
ctiv
e
wh
en
t
h
e
d
ataset
m
ee
ts
o
r
ap
p
r
o
x
im
ates
th
ese
p
r
o
b
ab
ilis
tic
as
s
u
m
p
tio
n
s
.
I
ts
f
ast tr
ain
in
g
an
d
i
n
f
er
en
ce
tim
es m
ak
e
NB
a
u
s
ef
u
l b
en
c
h
m
ar
k
f
o
r
p
r
o
b
ab
ilis
tic
class
if
ic
atio
n
m
o
d
els
[
2
5
]
:
(
\
)
=
(
)
∏
(
\
)
=
1
(
)
(
1
0
)
wh
er
e
th
e
p
r
io
r
p
r
o
b
ab
ilit
y
o
f
th
e
class
is
r
ep
r
esen
ted
b
y
(
)
,
(
\
)
is
th
e
p
r
o
b
a
b
ilit
y
o
f
a
f
ea
tu
r
e
an
d
is
th
e
g
iv
en
class
.
3
.
4
.
5
.
Dec
is
io
n t
re
e
DT
class
if
y
d
ata
b
y
r
ec
u
r
s
iv
ely
s
p
litt
in
g
th
e
f
ea
tu
r
e
s
p
ac
e
b
ased
o
n
th
r
esh
o
ld
s
th
at
m
ax
i
m
ize
clas
s
s
ep
ar
atio
n
[
2
6
]
.
I
n
(
1
2
)
s
h
o
ws
th
e
m
ath
em
atica
l f
o
r
m
u
la:
(
)
=
1
−
∑
(
\
)
2
=
1
(
1
1
)
wh
er
e
(
\
)
is
th
e
p
r
o
p
o
r
ti
o
n
o
f
cla
s
s
at
n
o
d
e
.
3
.
5
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n
I
n
t
h
i
s
s
t
u
d
y
,
d
i
f
f
e
r
e
n
t
m
e
t
r
i
c
s
h
a
v
e
b
e
e
n
u
s
e
d
t
o
e
v
a
l
u
a
t
e
c
l
a
s
s
i
f
i
c
a
t
i
o
n
o
f
e
a
c
h
M
L
m
o
d
e
l
[
2
7
]
,
[
2
8
]
.
i)
Acc
u
r
ac
y
:
th
is
m
etr
ic
r
ep
r
ese
n
ts
th
e
r
atio
o
f
co
r
r
ec
tly
class
i
f
ied
s
am
p
les
to
th
e
to
tal
n
u
m
b
er
o
f
s
am
p
les
[
2
0
]
.
I
t is ca
lcu
lated
as sh
o
wn
in
(
1
2
)
:
=
(
)
+
(
)
(
)
+
(
)
+
(
)
+
(
)
w
h
er
e,
d
en
o
tes
ac
cu
r
ac
y
,
T
P
an
d
T
N
ar
e
th
e
co
r
r
ec
tly
p
r
ed
icted
p
o
s
itiv
e
an
d
n
e
g
ativ
e
ca
s
es,
r
esp
ec
tiv
ely
,
wh
ile
FP
an
d
FN r
ep
r
esen
t f
alse p
r
e
d
icted
p
o
s
itiv
e
an
d
n
eg
ativ
e
ca
s
es.
ii)
Pre
cisi
o
n
: p
r
ec
is
io
n
is
th
e
r
atio
o
f
T
P p
r
ed
ictio
n
s
to
all
p
o
s
itiv
es p
r
ed
icted
,
as in
(
1
2
)
.
=
+
(
1
2
)
iii)
R
ec
all
(
s
en
s
itiv
ity
)
: in
(
1
3
)
r
e
p
r
esen
ts
th
e
r
ec
all
(
s
en
s
itiv
ity
)
an
d
in
d
icate
s
ac
tu
al
p
o
s
itiv
es.
=
+
(
1
3
)
i
v
)
F1
-
s
c
o
r
e
:
F
1
-
s
c
o
r
e
r
e
f
e
r
s
t
o
a
c
t
u
a
l
p
o
s
i
t
i
v
e
s
,
(
1
4
)
s
h
o
w
s
t
h
e
m
a
t
h
e
m
a
t
i
c
a
l
f
o
r
m
u
l
a
t
o
d
e
t
e
r
m
i
n
e
t
h
e
F
1
-
s
c
o
r
e
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
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tell
,
Vo
l.
1
4
,
No
.
5
,
Octo
b
er
2
0
2
5
:
4
0
0
6
-
4
0
1
6
4012
1
−
=
2
×
×
+
(
1
4
)
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
B
io
lo
g
ic
a
l f
ea
t
ures e
v
a
lua
t
io
n
T
ab
le
2
s
u
m
m
ar
izes
k
ey
s
tati
s
tical
an
d
p
er
f
o
r
m
an
ce
m
etr
ic
s
f
o
r
th
e
f
ea
tu
r
es,
in
clu
d
in
g
c
o
r
r
elatio
n
with
th
e
tar
g
et,
R
MS,
m
ea
n
,
STD,
SNR
,
an
d
AUC
f
r
o
m
th
e
ab
latio
n
s
tu
d
y
.
T
ab
le
2
to
g
e
th
er
with
Fig
u
r
e
2
also
h
ig
h
lig
h
t
f
ea
tu
r
e
r
elev
a
n
ce
.
B
asic
n
u
cleo
tid
e
co
u
n
ts
(
C
o
u
n
t_
A,
C
o
u
n
t_
T
,
C
o
u
n
t
_
C
,
C
o
u
n
t_
G)
an
d
GC
_
C
o
n
ten
t
s
h
o
w
th
e
s
tr
o
n
g
est
p
r
ed
ictiv
e
p
o
wer
,
with
C
o
u
n
t_
T
an
d
C
o
u
n
t_
A
p
o
s
itiv
ely
co
r
r
elate
d
an
d
C
o
u
n
t_
G
an
d
GC
_
C
o
n
ten
t
n
eg
ativ
ely
co
r
r
elate
d
with
class
if
icatio
n
.
Seq
u
en
ce
_
C
o
m
p
lex
ity
,
d
esp
ite
a
m
o
d
er
ate
AUC
(
0
.
7
3
8
0
)
,
h
as
a
h
ig
h
SNR
(
~4
2
)
,
in
d
icatin
g
s
tab
le,
v
alu
ab
le
in
p
u
t.
Seq
u
en
ce
_
Var
iab
ilit
y
h
as
lo
w
SNR
an
d
co
r
r
elatio
n
,
s
u
g
g
esti
n
g
lim
ited
s
tan
d
alo
n
e
u
s
ef
u
ln
ess
b
u
t p
o
s
s
ib
le
v
alu
e
w
h
e
n
co
m
b
i
n
ed
.
T
ab
le
2
.
Su
m
m
a
r
y
o
f
f
ea
tu
r
e
s
tatis
tic
s
an
d
p
er
f
o
r
m
an
ce
m
etr
ics f
r
o
m
ab
latio
n
s
tu
d
y
F
e
a
t
u
r
e
C
o
r
r
e
l
a
t
i
o
n
R
M
S
M
e
a
n
S
TD
S
N
R
AUC
C
o
u
n
t
_
C
-
0
.
1
4
5
4
9
1
3
.
4
1
7
1
3
.
0
6
6
3
.
0
6
2
1
4
.
2
6
7
0
.
7
6
8
2
C
o
u
n
t
_
A
0
.
2
5
4
3
8
1
5
.
3
0
5
1
4
.
8
5
8
3
.
6
8
9
4
.
0
2
8
0
.
7
4
1
9
C
o
u
n
t
_
T
0
.
2
6
6
6
4
1
6
.
6
1
3
1
6
.
1
5
1
3
.
9
1
0
4
4
.
1
3
0
.
7
5
0
8
C
o
u
n
t
_
G
-
0
.
4
3
6
5
1
1
3
.
3
9
1
2
.
9
2
5
3
.
5
1
7
8
3
.
6
7
4
0
.
7
3
9
G
C
_
C
o
n
t
e
n
t
-
0
.
4
3
6
6
8
0
.
4
6
2
8
0
.
4
5
5
9
7
0
.
0
7
9
5
9
5
.
7
2
9
0
.
7
5
4
S
e
q
u
e
n
c
e
_
C
o
mp
l
e
x
i
t
y
-
0
.
4
0
9
2
4
1
.
9
4
8
7
1
.
9
4
8
2
0
.
0
4
6
2
4
2
.
1
6
2
0
.
7
3
8
S
e
q
u
e
n
c
e
_
V
a
r
i
a
b
i
l
i
t
y
0
.
4
0
6
4
4
0
.
0
0
7
8
0
.
0
0
5
9
0
.
0
0
5
2
1
.
1
3
4
0
.
7
3
7
8
Fig
u
r
e
2
.
Featu
r
e
im
p
o
r
ta
n
ce
an
d
p
r
e
d
ictiv
e
v
alu
e
b
ased
o
n
AUC an
d
s
tatis
t
ical
s
tab
ilit
y
4
.
2
.
K
-
m
er
pa
t
t
er
n a
na
ly
s
is
K
-
m
er
an
aly
s
is
was
p
er
f
o
r
m
e
d
to
lin
k
s
h
o
r
t
n
u
cle
o
tid
e
m
o
tifs
with
p
r
o
m
o
ter
class
if
icati
o
n
.
E
ac
h
s
eq
u
en
ce
was
lab
eled
an
d
an
n
o
tated
with
its
to
p
th
r
ee
f
r
eq
u
en
t
3
-
m
er
s
,
wh
ich
wer
e
b
r
o
k
e
n
d
o
wn
in
to
k
-
m
er
s
to
ca
lcu
late
class
-
s
p
ec
if
ic
f
r
eq
u
en
cies.
Statis
tical
test
s
(
C
h
i
-
s
q
u
ar
e
o
r
Fis
h
er
’
s
e
x
ac
t)
ass
ess
ed
k
-
m
er
s
ig
n
if
ican
ce
ac
r
o
s
s
class
es.
W
h
ile
s
o
m
e
k
-
m
er
s
ap
p
ea
r
e
d
class
-
s
p
ec
if
ic
(
e.
g
.
,
'
aa
c,
ac
g
,
cg
c'
in
C
lass
0
;
'
aa
a,
ata,
taa'
in
C
lass
1
)
,
m
o
s
t
test
s
s
h
o
wed
n
o
n
-
s
ig
n
if
ica
n
t
r
esu
lts
,
lik
ely
d
u
e
to
s
m
all
s
am
p
le
s
ize
an
d
s
p
ar
s
e
d
ata
(
e.
g
.
,
C
h
i
-
s
q
u
ar
e
p
=1
.
0
0
0
0
)
.
T
h
ese
r
esu
lts
s
u
g
g
est
k
-
m
er
s
alo
n
e
h
av
e
lim
ited
d
is
cr
im
in
ativ
e
p
o
wer
b
u
t
ca
n
en
h
a
n
ce
m
o
d
els wh
en
co
m
b
in
ed
with
o
th
er
f
ea
t
u
r
es,
as illu
s
tr
ated
in
Fig
u
r
e
3
.
4
.
3
.
Cla
s
s
if
iers
f
o
r
eng
ineering
f
ea
t
ures
T
ab
le
3
an
d
Fig
u
r
e
4
s
h
o
w
th
e
p
er
f
o
r
m
an
ce
o
f
d
i
f
f
er
en
t
class
if
ier
s
u
s
in
g
b
asic
f
ea
tu
r
es.
SVM
ac
h
iev
ed
6
5
%
ac
c
u
r
ac
y
b
u
t
h
a
d
lo
w
s
p
ec
if
icity
(
0
.
5
6
)
a
n
d
m
o
d
er
ate
p
r
ec
is
io
n
(
0
.
6
1
)
d
esp
ite
g
o
o
d
s
en
s
itiv
ity
(
0
.
7
3
)
.
KNN
p
er
f
o
r
m
ed
p
o
o
r
ly
with
4
8
%
ac
cu
r
a
cy
an
d
v
er
y
l
o
w
s
p
ec
if
icity
(
0
.
2
5
)
,
s
tr
u
g
g
lin
g
to
class
if
y
n
_
PS
co
r
r
ec
tly
.
LR
s
h
o
wed
b
ala
n
ce
d
r
esu
lts
with
6
1
%
ac
cu
r
ac
y
an
d
0
.
5
s
p
ec
if
i
city
.
DT
s
p
er
f
o
r
m
ed
b
etter
,
r
ea
ch
in
g
7
1
%
ac
cu
r
ac
y
an
d
0
.
6
9
s
p
ec
if
icity
an
d
p
r
ec
i
s
io
n
.
NB
was
th
e
b
est,
ac
h
iev
i
n
g
9
0
%
ac
cu
r
ac
y
,
0
.
9
4
s
p
ec
if
icity
,
an
d
0
.
9
3
p
r
ec
is
io
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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tif
I
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tell
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N:
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8
9
3
8
A
n
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ve
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p
r
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mo
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ta
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n
a
lysi
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n
d
…
(
S
in
a
n
S
a
lim Mo
h
a
mme
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h
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t
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4013
Fig
u
r
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3
.
K
-
m
e
r
f
r
e
q
u
en
c
y
p
at
ter
n
s
T
ab
le
3
.
T
h
e
p
er
f
o
r
m
a
n
ce
m
et
r
ics u
s
in
g
b
asic f
ea
tu
r
es
M
o
d
e
l
s
A
c
c
u
r
a
c
y
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r
e
c
i
s
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o
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F1
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s
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o
r
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n
s
i
t
i
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t
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p
e
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i
f
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c
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t
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M
0
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5
6
0
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7
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0
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6
7
0
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6
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0
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5
K
N
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0
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2
5
0
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7
3
0
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5
8
0
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4
8
0
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4
8
LR
0
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5
0
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7
3
0
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6
5
0
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5
8
0
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6
1
DT
0
.
6
9
0
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7
3
0
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7
1
0
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6
9
0
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7
1
NB
0
.
8
7
0
.
9
0
.
9
3
0
.
9
0
.
9
4
Fig
u
r
e
4
.
T
h
e
p
er
f
o
r
m
a
n
ce
m
e
tr
ics o
f
class
if
ier
s
f
o
r
e
n
g
in
ee
r
in
g
b
asic f
ea
tu
r
es
T
ab
le
4
an
d
Fig
u
r
e
5
p
r
esen
t
r
esu
lts
u
s
in
g
th
e
n
ewly
d
ev
el
o
p
ed
f
ea
tu
r
es,
d
em
o
n
s
tr
atin
g
s
ig
n
if
ican
t
im
p
r
o
v
em
e
n
t
ac
r
o
s
s
class
if
ier
s
,
esp
ec
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f
o
r
th
o
s
e
th
at
s
t
r
u
g
g
led
with
b
asic
f
ea
tu
r
es.
E
n
h
an
ce
d
f
ea
tu
r
es
in
co
r
p
o
r
atin
g
d
o
m
ain
k
n
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ed
g
e
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d
h
ig
h
er
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o
r
d
e
r
s
eq
u
e
n
ce
in
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o
r
m
atio
n
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elp
e
d
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d
KNN
b
etter
ca
p
tu
r
e
n
o
n
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lin
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r
p
atter
n
s
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i
m
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r
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i
n
g
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r
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d
s
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icity
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s
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d
L
R
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ec
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r
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e
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e
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et
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o
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ted
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ier
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,
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ec
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m
in
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a
r
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o
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e
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m
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etitiv
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ef
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er
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eth
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d
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.
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.
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h
e
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r
m
a
n
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et
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s
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g
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ce
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ea
tu
r
e
ar
ch
itectu
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e
A
c
c
u
r
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c
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1
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4
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t
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p
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f
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t
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Fig
u
r
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5
.
T
h
e
p
er
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o
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m
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n
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e
tr
ics o
f
class
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ier
s
f
o
r
d
ev
elo
p
ed
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tu
r
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a
r
ch
itectu
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e
Fig
u
r
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6
co
m
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ar
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atin
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ch
a
r
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ter
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tic
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R
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r
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d
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etr
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r
class
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icatio
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r
es
F
ig
u
r
e
6
(
a)
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er
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th
e
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r
o
p
o
s
e
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en
h
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n
ce
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es
Fig
u
r
e
6
(
b
)
.
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h
e
im
p
r
o
v
ed
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ea
tu
r
e
s
et
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r
ly
b
o
o
s
ts
m
o
s
t
class
if
ier
s
’
p
er
f
o
r
m
an
ce
.
DT
ac
h
iev
e
a
s
o
lid
AUC
o
f
0
.
8
3
9
5
8
,
wh
ile
NB
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ch
es
a
p
e
r
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ec
t
1
.
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e
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ce
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ay
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RE
F
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NC
E
S
[
1
]
S
.
R
.
A
r
c
h
u
l
e
t
a
,
J.
A
.
G
o
o
d
r
i
c
h
,
a
n
d
J.
F
.
K
u
g
e
l
,
“
M
e
c
h
a
n
i
sms
a
n
d
f
u
n
c
t
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o
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s
o
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R
N
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p
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y
meras
e
I
I
g
e
n
e
r
a
l
t
r
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scr
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p
t
i
o
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h
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n
e
r
y
d
u
r
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t
h
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t
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scri
p
t
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y
c
l
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”
Bi
o
m
o
l
e
c
u
l
e
s
,
v
o
l
.
1
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,
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o
.
2
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F
e
b
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
b
i
o
m
1
4
0
2
0
1
7
6
.
[
2
]
J.
Y
u
a
n
e
t
a
l
.
,
“
A
c
o
m
p
e
n
d
i
u
m
o
f
g
e
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e
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v
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a
t
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sso
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4
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s,
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e
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o
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1
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4
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6.
[
3
]
J.
B
l
a
z
e
c
k
a
n
d
H
.
S
.
A
l
p
e
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,
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J
o
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n
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.
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p
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.
[
4
]
G
.
B
r
i
x
i
e
t
a
l
.
,
“
G
e
n
o
m
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m
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d
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E
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,
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o
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:
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1
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0
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/
2
0
2
5
.
0
2
.
1
8
.
6
3
8
9
1
8
.
[
5
]
R
.
K
.
U
mar
o
v
a
n
d
V
.
V
.
S
o
l
o
v
y
e
v
,
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t
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o
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o
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