I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
37
,
No
.
2
,
Feb
r
u
ar
y
20
25
,
p
p
.
1
2
9
4
~1
3
0
5
I
SS
N:
2
502
-
4
7
52
,
DOI
: 1
0
.
1
1
5
9
1
/ijee
cs
.v
37.
i
2
.
p
p
1
2
9
4
-
1
3
0
5
1294
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs
.
ia
esco
r
e.
co
m
H
y
dro
pho
bicity s
ig
na
l ana
ly
sis
f
o
r robus
t
SA
RS
-
Co
V
-
2
cla
ss
ificatio
n
M
o
ha
m
m
a
d J
a
m
hu
ri
1,
2
,
M
o
ha
m
m
a
d Is
a
I
ra
wa
n
1
,
I
m
a
m Mu
k
hla
s
h
1
,
Ni
Ny
o
m
a
n T
ri
P
us
pa
ni
ng
s
ih
3
1
D
e
p
a
r
t
me
n
t
o
f
M
a
t
h
e
m
a
t
i
c
s
,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
D
a
t
a
A
n
a
l
y
t
i
c
s,
I
n
st
i
t
u
t
Te
k
n
o
l
o
g
i
S
e
p
u
l
u
h
N
o
p
e
mb
e
r
,
S
u
r
a
b
a
y
a
,
I
n
d
o
n
e
si
a
2
D
e
p
a
r
t
me
n
t
o
f
M
a
t
h
e
m
a
t
i
c
s
,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
si
t
a
s I
sl
a
m N
e
g
e
r
i
M
a
u
l
a
n
a
M
a
l
i
k
I
b
r
a
h
i
m,
M
a
l
a
n
g
,
I
n
d
o
n
e
s
ia
3
D
e
p
a
r
t
me
n
t
o
f
C
h
e
m
i
st
r
y
,
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
a
n
d
Te
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
si
t
a
s
A
i
r
l
a
n
g
g
a
,
S
u
r
a
b
a
y
a
,
I
n
d
o
n
e
si
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
25
,
2
0
2
4
R
ev
is
ed
Sep
24
,
2
0
2
4
Acc
ep
ted
Sep
30
,
2
0
2
4
Ra
p
id
a
n
d
a
c
c
u
ra
te
c
las
sifica
ti
o
n
o
f
v
iral
p
a
t
h
o
g
e
n
s
is
c
rit
ica
l
fo
r
e
ffe
c
ti
v
e
p
u
b
li
c
h
e
a
lt
h
i
n
terv
e
n
t
io
n
s.
Th
is
stu
d
y
i
n
tr
o
d
u
c
e
s
a
n
o
v
e
l
a
p
p
r
o
a
c
h
u
sin
g
c
o
n
v
o
lu
ti
o
n
a
l
n
e
u
ra
l
n
e
two
r
k
s
(
CNN
)
to
c
las
sify
S
AR
S
-
Co
V
-
2
a
n
d
n
o
n
-
S
ARS
-
Co
V
-
2
v
ir
u
se
s
v
ia
h
y
d
ro
p
h
o
b
icity
sig
n
a
l
d
e
ri
v
e
d
fr
o
m
DN
A
se
q
u
e
n
c
e
s.
Co
n
v
e
n
ti
o
n
a
l
m
a
c
h
i
n
e
lea
rn
in
g
m
e
th
o
d
s
g
ra
p
p
le
with
th
e
v
a
riab
il
it
y
o
f
v
iral
g
e
n
e
ti
c
m
a
teria
l,
re
q
u
iri
n
g
fi
x
e
d
-
len
g
t
h
se
q
u
e
n
c
e
s
a
n
d
e
x
ten
siv
e
p
re
p
ro
c
e
ss
in
g
.
Th
e
p
ro
p
o
se
d
m
e
th
o
d
tra
n
sfo
rm
s
g
e
n
e
ti
c
se
q
u
e
n
c
e
s
in
to
ima
g
e
-
b
a
se
d
re
p
re
se
n
tati
o
n
s,
e
n
a
b
li
n
g
CNN
s
t
o
h
a
n
d
le
c
o
m
p
l
e
x
it
y
a
n
d
v
a
riab
il
it
y
wit
h
o
u
t
t
h
e
se
c
o
n
str
a
i
n
ts.
Th
e
d
a
tas
e
t
in
c
lu
d
e
s
8
,
1
4
3
DN
A
se
q
u
e
n
c
e
s
fro
m
se
v
e
n
c
o
r
o
n
a
v
ir
u
se
s,
tran
sla
ted
in
t
o
a
m
in
o
a
c
id
se
q
u
e
n
c
e
s
a
n
d
e
v
a
lu
a
ted
f
o
r
h
y
d
ro
p
h
o
b
icity
.
Ex
p
e
rime
n
tal
re
su
lt
s
d
e
m
o
n
stra
t
e
th
a
t
th
e
CNN
m
o
d
e
l
a
c
h
iev
e
s
su
p
e
rio
r
p
e
rfo
rm
a
n
c
e
,
wit
h
a
n
a
c
c
u
ra
c
y
o
f
o
v
e
r
9
9
.
8
4
%
in
t
h
e
c
las
sifica
ti
o
n
tas
k
.
Th
e
m
o
d
e
l
a
lso
p
e
rfo
rm
s
we
ll
with
e
x
ten
d
e
d
se
q
u
e
n
c
e
len
g
t
h
s,
s
h
o
wc
a
sin
g
ro
b
u
st
n
e
ss
a
n
d
a
d
a
p
tab
il
it
y
.
Co
m
p
a
re
d
to
p
re
v
io
u
s
stu
d
ies
,
th
is
m
e
th
o
d
o
ffe
rs
h
i
g
h
e
r
a
c
c
u
ra
c
y
a
n
d
c
o
m
p
u
tati
o
n
a
l
e
fficie
n
c
y
,
p
ro
v
i
d
in
g
a
re
li
a
b
le
so
lu
ti
o
n
f
o
r
ra
p
id
v
iru
s
d
e
tec
ti
o
n
wi
th
p
o
te
n
ti
a
l
a
p
p
li
c
a
ti
o
n
s in
b
i
o
in
f
o
rm
a
ti
c
s a
n
d
c
li
n
ica
l
s
e
tt
in
g
s.
K
ey
w
o
r
d
s
:
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
Gen
etic
s
eq
u
en
cin
g
Ky
te
-
Do
o
litt
le
s
ca
le
Ma
ch
in
e
lear
n
in
g
Vir
u
s
id
en
tific
atio
n
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
:
Mo
h
am
m
ad
I
s
a
I
r
awa
n
Dep
ar
tm
en
t o
f
Ma
th
em
atics,
Facu
lty
o
f
Scien
ce
a
n
d
Data
An
aly
tics
I
n
s
titu
t
T
ek
n
o
lo
g
i Sep
u
lu
h
No
p
em
b
er
Su
r
ab
ay
a,
I
n
d
o
n
esia
E
m
ail: m
ii@
it
s
.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
Gen
o
m
ics
h
as
tr
an
s
f
o
r
m
ed
t
h
e
u
n
d
e
r
s
tan
d
in
g
o
f
b
io
lo
g
ical
p
r
o
ce
s
s
es
an
d
d
is
ea
s
e
m
ec
h
an
is
m
s
.
Hig
h
-
th
r
o
u
g
h
p
u
t
s
eq
u
en
cin
g
tech
n
o
lo
g
ies
h
av
e
g
e
n
er
ate
d
v
ast
am
o
u
n
ts
o
f
g
en
o
m
ic
d
ata,
n
ec
ess
itatin
g
ad
v
an
ce
d
co
m
p
u
tatio
n
al
m
et
h
o
d
s
f
o
r
an
aly
s
is
[
1
]
.
T
h
e
r
ap
id
m
u
tatio
n
a
n
d
s
p
r
ea
d
o
f
v
ir
u
s
es
lik
e
SAR
S
-
C
o
V
-
2
u
n
d
e
r
s
co
r
e
th
e
u
r
g
e
n
cy
f
o
r
r
o
b
u
s
t g
en
o
m
ic
an
aly
s
is
tech
n
i
q
u
es to
tr
ac
k
e
v
o
lv
in
g
p
ath
o
g
e
n
s
[
2
]
.
H
y
d
r
o
p
h
o
b
i
c
i
t
y
,
a
f
u
n
d
a
m
e
n
t
a
l
p
r
o
p
e
r
t
y
o
f
a
m
i
n
o
a
c
i
d
s
,
s
i
g
n
i
f
i
c
a
n
t
l
y
i
m
p
ac
ts
p
r
o
t
e
i
n
f
o
l
d
i
n
g
,
s
t
a
b
il
i
t
y
,
a
n
d
i
n
t
e
r
a
c
t
i
o
n
s
[
3
]
.
H
y
d
r
o
p
h
o
b
i
c
i
n
t
e
r
a
c
ti
o
n
s
,
w
h
e
r
e
n
o
n
-
p
o
l
a
r
a
m
i
n
o
a
c
i
d
r
es
i
d
u
e
s
cl
u
s
te
r
a
w
a
y
f
r
o
m
w
a
t
e
r
,
h
e
l
p
m
a
i
n
t
ai
n
p
r
o
t
e
i
n
s
ta
b
i
l
it
y
a
n
d
f
a
c
il
i
t
at
e
p
r
o
p
e
r
f
o
l
d
i
n
g
[4
]
.
A
d
e
t
ai
l
e
d
u
n
d
e
r
s
t
a
n
d
i
n
g
o
f
t
h
e
h
y
d
r
o
p
h
o
b
i
c
p
r
o
f
i
l
e
o
f
p
r
o
t
e
i
n
s
is
es
s
e
n
ti
a
l
f
o
r
r
e
v
e
a
l
i
n
g
t
h
e
m
e
c
h
a
n
is
m
s
b
e
h
i
n
d
t
h
e
i
r
s
t
r
u
ct
u
r
e
a
n
d
f
u
n
c
t
io
n
[
5
]
.
Hy
d
r
o
p
h
o
b
ic
p
r
o
tein
r
eg
i
o
n
s
m
ain
tain
s
tr
u
ctu
r
al
in
teg
r
it
y
b
y
b
ein
g
en
clo
s
ed
with
in
th
e
co
r
e,
co
n
t
r
ib
u
tin
g
to
s
tab
ilit
y
an
d
t
h
r
ee
-
d
im
en
s
io
n
al
c
o
n
f
o
r
m
atio
n
[
6
]
.
T
h
ese
r
eg
io
n
s
ar
e
cr
u
cial
f
o
r
p
r
o
tein
-
p
r
o
tein
a
n
d
p
r
o
t
e
i
n
-
l
i
g
a
n
d
i
n
t
e
r
a
c
t
i
o
n
s
,
v
i
t
a
l
f
o
r
u
n
d
e
r
s
t
a
n
d
i
n
g
v
i
r
a
l
m
e
c
h
a
n
i
s
m
s
a
n
d
i
d
e
n
t
i
f
y
i
n
g
t
h
e
r
a
p
e
u
t
i
c
t
a
r
g
e
t
s
[
7
]
.
Ma
ch
in
e
lear
n
in
g
m
o
d
els
ca
n
lev
er
ag
e
h
y
d
r
o
p
h
o
b
icity
p
r
o
f
iles
as
b
io
lo
g
ically
r
elev
an
t
f
ea
tu
r
es,
ef
f
ec
tiv
ely
r
e
p
r
e
s
e
n
t
i
n
g
g
e
n
e
t
i
c
i
n
f
o
r
m
a
t
i
o
n
a
n
d
f
a
c
i
l
i
t
a
t
i
n
g
d
a
t
a
a
n
a
l
y
s
i
s
t
h
r
o
u
g
h
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s
(
C
N
N
s
)
[
8
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Hyd
r
o
p
h
o
b
icity
s
ig
n
a
l a
n
a
lysi
s
fo
r
r
o
b
u
s
t S
A
R
S
-
C
o
V
-
2
cla
s
s
ifica
tio
n
(
Mo
h
a
mma
d
J
a
mh
u
r
i)
1295
B
y
co
n
v
er
tin
g
g
en
etic
s
eq
u
e
n
ce
s
in
to
h
y
d
r
o
p
h
o
b
icity
-
b
ased
v
is
u
al
r
ep
r
esen
tatio
n
s
,
th
e
co
m
p
lex
ity
o
f
v
ir
al
g
en
etic
d
ata
ca
n
b
e
m
an
ag
e
d
m
o
r
e
e
f
f
icien
tly
,
s
u
r
p
ass
in
g
th
e
ca
p
ab
ilit
ies
o
f
tr
ad
itio
n
al
s
eq
u
en
ce
-
b
ased
m
eth
o
d
s
[
5
]
.
C
lass
if
y
i
n
g
S
AR
S
-
C
o
V
-
2
a
n
d
n
o
n
-
SAR
S
-
C
o
V
-
2
s
e
q
u
e
n
c
es
is
c
r
u
ci
al
f
o
r
u
n
d
e
r
s
t
a
n
d
in
g
v
i
r
a
l
g
e
n
o
m
es
a
n
d
h
as
s
i
g
n
if
i
ca
n
t
i
m
p
lic
ati
o
n
s
f
o
r
d
r
u
g
d
is
c
o
v
e
r
y
[
9
]
.
A
cc
u
r
at
e
cl
ass
i
f
ic
a
tio
n
h
el
p
s
i
d
e
n
t
if
y
co
n
s
e
r
v
e
d
r
e
g
i
o
n
s
w
it
h
i
n
t
h
e
S
AR
S
-
C
o
V
-
2
g
e
n
o
m
e
,
ess
en
tia
l
f
o
r
d
e
v
el
o
p
i
n
g
a
n
ti
v
ir
al
d
r
u
g
s
tar
g
eti
n
g
k
e
y
v
i
r
al
f
u
n
cti
o
n
s
a
n
d
r
e
p
l
ic
ati
o
n
p
r
o
c
ess
es
[
1
0
]
.
T
h
is
c
lass
i
f
i
ca
t
io
n
also
a
id
s
i
n
m
o
n
i
to
r
i
n
g
m
u
ta
ti
o
n
s
t
h
a
t
m
a
y
af
f
e
ct
t
h
e
v
i
r
u
s
'
s
s
u
s
c
e
p
t
i
b
i
l
i
t
y
t
o
t
r
e
a
t
m
e
n
t
s
,
g
u
i
d
i
n
g
t
h
e
d
e
v
e
l
o
p
m
e
n
t
o
f
e
f
f
e
c
t
i
v
e
d
r
u
g
s
a
g
a
i
n
s
t
e
m
e
r
g
i
n
g
v
a
r
i
a
n
t
s
[
1
1
]
.
Fu
r
th
e
r
m
o
r
e
,
i
n
s
i
g
h
ts
f
r
o
m
t
h
i
s
c
lass
i
f
i
ca
t
io
n
c
a
n
i
n
f
o
r
m
t
h
e
d
esi
g
n
o
f
b
r
o
ad
-
s
p
e
ct
r
u
m
a
n
t
iv
i
r
a
ls
b
y
r
e
v
e
ali
n
g
co
m
m
o
n
v
u
l
n
e
r
a
b
i
liti
es
ac
r
o
s
s
d
if
f
e
r
e
n
t
v
i
r
u
s
es,
w
h
ic
h
is
cr
u
c
ial
f
o
r
p
r
e
p
a
r
i
n
g
ag
ai
n
s
t
f
u
tu
r
e
c
o
r
o
n
a
v
ir
u
s
o
u
t
b
r
ea
k
s
[
1
2
]
.
A
d
d
it
io
n
all
y
,
ac
c
u
r
at
e
class
if
ica
ti
o
n
ass
is
ts
in
id
e
n
ti
f
y
i
n
g
e
p
it
o
p
es
f
o
r
v
a
cc
i
n
e
d
e
v
el
o
p
m
e
n
t,
ac
c
ele
r
a
tes
d
r
u
g
d
is
c
o
v
e
r
y
t
h
r
o
u
g
h
h
i
g
h
-
t
h
r
o
u
g
h
p
u
t
s
cr
ee
n
i
n
g
a
n
d
s
tr
u
ct
u
r
e
-
b
as
e
d
d
esi
g
n
,
an
d
u
lti
m
a
tel
y
l
ea
d
s
to
m
o
r
e
e
f
f
ec
t
iv
e
t
h
e
r
a
p
e
u
ti
c
i
n
te
r
v
e
n
t
io
n
s
a
n
d
i
m
p
r
o
v
e
d
p
u
b
li
c
h
e
a
lt
h
o
u
t
c
o
m
es
[
1
3
]
.
T
r
ad
i
tio
n
al
m
eth
o
d
s
,
s
u
ch
as
p
o
ly
m
er
ase
ch
ain
r
ea
ctio
n
(
P
C
R
)
[
3
]
,
s
eq
u
en
cin
g
[
4
]
,
a
n
d
s
er
o
lo
g
ical
ass
ay
s
[
5
]
,
o
f
ten
g
r
ap
p
le
with
th
e
co
m
p
lex
ity
an
d
v
ar
ia
b
ilit
y
o
f
v
ir
al
g
e
n
etic
m
ater
ial,
u
n
d
er
s
co
r
in
g
t
h
e
n
ee
d
f
o
r
ad
v
an
ce
d
c
o
m
p
u
tatio
n
al
ap
p
r
o
ac
h
es.
E
ar
ly
m
ac
h
in
e
-
le
ar
n
in
g
tech
n
iq
u
es
ap
p
lied
to
v
ir
al
s
tu
d
ies
h
av
e
ty
p
ically
f
o
cu
s
ed
o
n
th
e
d
ir
ec
t
an
aly
s
is
o
f
DNA
o
r
R
N
A
s
e
q
u
en
ce
s
[
1
4
]
.
T
h
ese
tech
n
iq
u
es
ty
p
ically
r
eq
u
ir
e
f
ix
ed
in
p
u
t le
n
g
th
s
an
d
in
v
o
lv
e
ex
ten
s
iv
e
p
r
ep
r
o
ce
s
s
in
g
to
ex
tr
ac
t m
ea
n
in
g
f
u
l f
ea
tu
r
es f
r
o
m
g
en
etic
d
ata
[
1
5
]
.
C
o
n
v
en
tio
n
al
s
eq
u
en
ce
-
b
ase
d
m
ac
h
in
e
lear
n
in
g
m
o
d
els
[
1
6
]
f
ac
e
s
ig
n
if
ican
t
ch
allen
g
es
in
h
an
d
lin
g
th
e
v
ar
iab
ilit
y
an
d
co
m
p
lex
ity
o
f
v
ir
al
g
en
etic
m
ater
ial,
lim
itin
g
th
eir
ef
f
ec
tiv
en
ess
an
d
ad
a
p
tab
ilit
y
.
Stu
d
ies
on
m
etag
en
o
m
ic
s
eq
u
en
ce
class
i
f
icatio
n
[
1
7
]
h
ig
h
lig
h
t
th
ese
lim
itatio
n
s
an
d
u
n
d
e
r
s
co
r
e
th
e
n
ee
d
f
o
r
m
o
r
e
f
lex
ib
le
an
d
ef
f
icien
t
s
o
lu
tio
n
s
.
Mo
r
eo
v
er
,
th
e
n
ee
d
f
o
r
u
n
if
o
r
m
s
eq
u
en
ce
len
g
th
s
an
d
ex
p
er
t
-
d
r
iv
e
n
f
ea
tu
r
e
s
elec
tio
n
ca
n
d
elay
cr
itical
r
es
p
o
n
s
es d
u
r
in
g
o
u
tb
r
ea
k
s
.
R
ec
en
t
ad
v
an
ce
m
e
n
ts
in
d
ee
p
lear
n
in
g
,
p
ar
ticu
lar
ly
C
NNs,
o
f
f
er
p
r
o
m
is
in
g
s
o
l
u
tio
n
s
b
y
tr
an
s
f
o
r
m
in
g
g
en
etic
s
eq
u
en
ce
s
in
to
im
ag
e
-
b
ased
r
ep
r
esen
tatio
n
s
[
1
8
]
,
[
1
9
]
.
T
h
ese
ap
p
r
o
ac
h
es
en
ab
le
h
an
d
lin
g
c
o
m
p
lex
a
n
d
v
ar
ia
b
le
g
en
etic
d
ata
m
o
r
e
ef
f
ec
tiv
ely
th
an
tr
ad
itio
n
al
m
eth
o
d
s
.
Ho
wev
er
,
p
r
e
v
io
u
s
s
tu
d
ies
h
av
e
n
o
t
f
o
c
u
s
ed
o
n
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als,
leav
i
n
g
a
g
ap
in
lev
e
r
ag
in
g
th
is
b
io
lo
g
ically
r
elev
a
n
t
f
ea
tu
r
e
f
o
r
g
e
n
o
m
ic
an
aly
s
is
.
B
y
in
teg
r
atin
g
h
y
d
r
o
p
h
o
b
ici
ty
p
r
o
f
iles
in
to
th
e
an
aly
s
is
,
we
ca
n
p
o
ten
tially
im
p
r
o
v
e
t
h
e
ac
cu
r
ac
y
an
d
ef
f
i
cien
cy
o
f
v
ir
al
class
if
icatio
n
.
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
a
n
o
v
el
a
p
p
r
o
ac
h
to
v
ir
al
cla
s
s
if
icatio
n
b
y
lev
e
r
ag
in
g
C
NNs
an
d
h
y
d
r
o
p
h
o
b
icity
im
ag
es
d
er
iv
e
d
f
r
o
m
DNA
s
eq
u
en
ce
s
.
B
y
tr
an
s
f
o
r
m
in
g
g
en
etic
s
eq
u
en
ce
s
in
to
h
y
d
r
o
p
h
o
b
icity
-
b
ased
v
is
u
al
r
ep
r
esen
tatio
n
s
,
we
en
a
b
le
C
NNs
to
h
an
d
le
v
ir
al
g
en
etic
d
ata'
s
in
h
er
en
t
co
m
p
lex
ity
a
n
d
v
ar
iab
ilit
y
m
o
r
e
ef
f
ec
tiv
ely
.
T
h
is
ap
p
r
o
ac
h
r
ed
u
ce
s
th
e
n
ee
d
f
o
r
ex
ten
s
iv
e
p
r
ep
r
o
ce
s
s
in
g
an
d
f
i
x
ed
i
n
p
u
t
len
g
th
s
,
p
r
o
v
id
i
n
g
a
f
lex
ib
le
an
d
e
f
f
icien
t so
lu
tio
n
f
o
r
v
ir
al
class
if
icatio
n
.
Ou
r
wo
r
k
p
r
esen
ts
s
ev
er
al
u
n
iq
u
e
c
o
n
tr
ib
u
tio
n
s
th
at
d
i
s
tin
g
u
is
h
it
f
r
o
m
e
x
is
tin
g
liter
atu
r
e:
“
i)
in
n
o
v
ativ
e
u
s
e
o
f
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als:
we
in
tr
o
d
u
ce
a
n
o
v
el
m
eth
o
d
f
o
r
r
e
p
r
esen
tin
g
g
en
etic
in
f
o
r
m
atio
n
u
s
in
g
h
y
d
r
o
p
h
o
b
i
city
s
ig
n
als
d
er
i
v
ed
f
r
o
m
DN
A
s
eq
u
en
ce
s
,
a
f
ea
tu
r
e
n
o
t
ex
p
lo
r
ed
b
y
tr
ad
itio
n
al
n
u
cleo
tid
e
-
b
ased
m
eth
o
d
s
,
ii)
ap
p
licatio
n
o
f
C
NN:
l
ev
er
ag
in
g
C
NNs,
we
tr
an
s
f
o
r
m
g
en
etic
s
eq
u
en
ce
s
in
to
h
y
d
r
o
p
h
o
b
icity
-
b
ased
v
is
u
al
r
ep
r
esen
tatio
n
s
,
en
h
a
n
cin
g
t
h
e
m
o
d
el’
s
ab
ilit
y
to
p
r
o
ce
s
s
co
m
p
lex
an
d
v
ar
ia
b
le
v
ir
al
g
en
etic
d
ata
,
iii)
im
p
r
o
v
ed
f
lex
ib
ilit
y
an
d
ef
f
icien
cy
:
o
u
r
m
eth
o
d
ad
d
r
ess
es
th
e
lim
i
tatio
n
s
o
f
co
n
v
en
tio
n
al
s
eq
u
en
ce
-
b
ased
m
o
d
els,
s
u
ch
as
th
e
r
eq
u
ir
em
en
t
f
o
r
f
ix
ed
s
eq
u
e
n
ce
len
g
th
s
an
d
ex
ten
s
iv
e
f
ea
tu
r
e
ex
tr
ac
tio
n
,
o
f
f
er
in
g
a
m
o
r
e
ad
a
p
tab
le
an
d
ef
f
icien
t a
p
p
r
o
ac
h
to
v
i
r
al
class
if
icatio
n
.
T
h
e
r
em
ain
d
er
o
f
th
is
p
a
p
er
i
s
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
s
ec
tio
n
2
d
etails
th
e
m
eth
o
d
o
lo
g
y
,
in
clu
d
in
g
d
ata
co
llectio
n
,
p
r
e
p
ar
atio
n
,
m
o
d
el
im
p
lem
en
tatio
n
,
an
d
e
x
p
er
im
en
tal
s
etu
p
.
Sectio
n
3
p
r
esen
ts
th
e
r
esu
lts
an
d
d
is
cu
s
s
io
n
,
co
m
p
a
r
in
g
th
e
p
er
f
o
r
m
an
ce
o
f
o
u
r
C
NN
-
b
ased
ap
p
r
o
ac
h
with
b
aselin
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els an
d
p
r
ev
io
u
s
s
tu
d
ies.
Fin
ally
,
Sectio
n
4
co
n
clu
d
es t
h
e
p
ap
er
an
d
s
u
g
g
ests
f
u
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
.
2.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
o
u
tlin
es
th
e
d
ata
co
llectio
n
,
p
r
ep
r
o
ce
s
s
in
g
,
m
o
d
el
im
p
lem
en
tatio
n
,
an
d
ex
p
er
im
en
tal
s
etu
p
u
s
ed
in
th
is
s
tu
d
y
.
T
h
e
f
o
cu
s
is
o
n
class
if
y
in
g
SA
R
S
-
C
o
V
-
2
an
d
n
o
n
-
SAR
S
-
C
o
V
-
2
v
ir
u
s
es
u
s
in
g
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NN)
with
h
y
d
r
o
p
h
o
b
ic
ity
s
ig
n
als
d
er
iv
e
d
f
r
o
m
D
NA
s
eq
u
en
ce
s
.
T
h
e
ap
p
r
o
ac
h
ad
d
r
ess
es th
e
lim
itat
io
n
s
o
f
co
n
v
en
tio
n
al
m
eth
o
d
s
th
at
r
eq
u
ir
e
f
ix
ed
-
le
n
g
th
s
eq
u
e
n
ce
s
an
d
ex
ten
s
iv
e
f
ea
tu
r
e
ex
tr
ac
tio
n
p
r
e
p
r
o
ce
s
s
in
g
.
2
.
1
.
Da
t
a
c
o
llect
io
n
W
e
co
llected
DNA
s
eq
u
en
ce
s
o
f
s
ev
en
co
r
o
n
a
v
ir
u
s
es,
ca
teg
o
r
ized
i
n
to
SAR
S
-
C
o
V
-
2
an
d
n
o
n
-
SAR
S
-
C
o
V
-
2
class
es.
T
h
ese
s
eq
u
en
ce
s
wer
e
o
b
tain
ed
f
r
o
m
th
e
NC
B
I
v
ir
u
s
d
ata
b
ase
b
y
s
ea
r
ch
in
g
f
o
r
th
e
d
esi
r
ed
v
ir
u
s
t
y
p
e
at
www.
n
cb
i.n
lm
.
n
ih
.
g
o
v
/lab
s
/v
ir
u
s
.
T
h
e
d
ataset
co
m
p
r
is
es
8
,
1
4
3
s
am
p
les,
with
4
,
1
5
6
class
if
ied
as
n
o
n
-
SAR
S
-
C
o
V
-
2
an
d
3
,
9
8
7
as
SAR
S
-
C
o
V
-
2
.
T
h
ese
s
eq
u
en
ce
len
g
th
s
r
an
g
e
d
f
r
o
m
8
to
3
1
,
1
0
4
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
2
,
Feb
r
u
a
r
y
20
25
:
1
2
9
4
-
1
3
0
5
1296
b
ase
p
air
s
(
b
p
s
)
.
Deta
iled
in
f
o
r
m
atio
n
,
in
cl
u
d
in
g
t
h
e
n
u
m
b
er
o
f
s
eq
u
en
ce
s
an
d
t
h
eir
len
g
th
r
an
g
es
f
o
r
ea
ch
v
ir
u
s
ty
p
e,
is
s
u
m
m
ar
ize
d
in
T
ab
le
1
.
T
ab
le
1
.
Deta
iled
d
escr
ip
tio
n
o
f
th
e
co
r
o
n
a
v
ir
u
s
d
ataset
N
a
mes
N
u
mb
e
r
s
o
f
se
q
u
e
n
c
e
s
M
i
n
.
l
e
n
g
t
h
M
a
x
.
l
e
n
g
t
h
C
l
a
s
s
H
C
o
V
-
O
C
4
3
1
,
1
4
9
8
3
1
,
1
0
4
0
H
C
o
V
-
2
2
9
E
6
1
9
20
2
8
,
7
5
4
0
H
C
o
V
-
H
K
U
1
4
1
2
81
3
0
,
1
4
4
0
H
C
o
V
-
N
L6
3
6
6
9
81
2
7
,
8
3
3
0
M
E
R
S
-
C
o
V
1
,
2
5
9
1
1
0
3
0
,
4
8
4
0
S
A
R
S
-
C
o
V
18
1
5
8
2
9
,
7
5
1
0
S
A
R
S
-
C
o
V
-
2
3
,
9
8
7
2
7
8
2
9
,
9
0
9
1
2
.
2
.
Da
t
a
prepa
ra
t
i
o
n
T
h
e
DNA
s
eq
u
en
ce
s
,
c
o
n
s
is
tin
g
o
f
th
e
n
u
cle
o
tid
e
b
ases
ad
e
n
in
e
(
A)
,
cy
to
s
in
e
(
C
)
,
g
u
an
in
e
(
G)
,
a
n
d
th
y
m
in
e
(
T
)
,
wer
e
tr
an
s
lated
in
to
am
in
o
ac
id
s
eq
u
en
ce
s
u
s
in
g
th
e
s
tan
d
ar
d
g
en
etic
co
d
e.
T
h
is
tr
an
s
latio
n
in
v
o
lv
es
r
ea
d
i
n
g
th
e
DNA
s
eq
u
en
ce
in
tr
ip
lets
o
f
n
u
cleo
ti
d
es,
k
n
o
wn
as
co
d
o
n
s
,
ea
ch
co
r
r
esp
o
n
d
in
g
t
o
a
s
p
ec
if
ic
am
in
o
ac
id
o
r
a
s
to
p
s
ig
n
al
[
2
0
]
.
Giv
en
a
DNA
s
eq
u
en
ce
=
{
1
,
2
,
…
,
}
wh
er
e
r
ep
r
esen
ts
th
e
-
th
n
u
cleo
ti
d
e,
th
e
tr
an
s
latio
n
f
u
n
ctio
n
co
n
v
e
r
ts
ev
er
y
tr
i
p
l
et
(
3
−
2
,
3
−
1
,
3
)
in
to
a
n
a
m
in
o
ac
id
u
n
til
th
e
en
d
o
f
th
e
s
eq
u
en
ce
is
r
ea
ch
ed
.
I
f
is
n
o
t
a
m
u
ltip
le
o
f
3
,
th
e
r
e
m
ain
in
g
n
u
cleo
ti
d
es
ar
e
ig
n
o
r
e
d
.
Ma
th
em
atica
lly
,
th
e
tr
an
s
latio
n
ca
n
b
e
d
escr
ib
ed
as in
(
1)
:
=
(
)
=
{
(
3
−
2
,
3
−
1
,
3
)
|
=
1
,
2
,
…
,
⌊
3
⌋
}
(
1
)
wh
er
e
T
is
th
e
m
ap
p
in
g
f
u
n
cti
o
n
f
r
o
m
co
d
o
n
s
to
a
m
in
o
ac
id
s
b
ased
o
n
th
e
s
tan
d
a
r
d
g
e
n
eti
c
co
d
e.
Hy
d
r
o
p
h
o
b
icity
p
r
o
f
iles
wer
e
g
en
er
ated
f
o
r
ea
ch
am
in
o
ac
id
s
eq
u
en
ce
to
cr
ea
te
i
m
ag
e
-
b
ased
r
ep
r
esen
tatio
n
s
.
W
e
em
p
lo
y
e
d
th
e
Ky
te
-
Do
o
litt
le
s
ca
le
[
2
1
]
,
a
wid
ely
u
s
ed
m
ea
s
u
r
e
f
o
r
h
y
d
r
o
p
h
o
b
icity
,
to
q
u
an
tify
th
e
h
y
d
r
o
p
h
o
b
icity
o
f
am
in
o
ac
id
r
esid
u
es.
Ma
th
em
atica
lly
,
th
e
h
y
d
r
o
p
h
o
b
icit
y
v
alu
e
H
f
o
r
ea
ch
am
in
o
ac
id
i
is
r
ep
r
esen
ted
as
,
wh
er
e
i
d
en
o
tes
th
e
p
o
s
itio
n
in
th
e
am
in
o
ac
id
s
eq
u
en
ce
.
Giv
en
an
am
in
o
ac
id
s
eq
u
en
ce
{
1
,
2
,
…
,
}
,
wh
er
e
n
is
th
e
len
g
th
o
f
th
e
s
eq
u
en
ce
,
ea
c
h
am
in
o
ac
id
is
tr
an
s
f
o
r
m
ed
in
t
o
its
co
r
r
esp
o
n
d
i
n
g
h
y
d
r
o
p
h
o
b
i
city
v
alu
e
u
s
in
g
th
e
Ky
te
-
Do
o
litt
le
s
ca
le.
T
h
e
h
y
d
r
o
p
h
o
b
ic
ity
p
r
o
f
ile
f
o
r
th
e
en
tire
s
eq
u
en
ce
ca
n
b
e
r
e
p
r
ese
n
ted
in
(
2
)
:
=
{
1
,
2
,
…
,
}
(
2
)
T
o
co
n
v
er
t
th
e
lin
ea
r
h
y
d
r
o
p
h
o
b
icity
p
r
o
f
ile
i
n
to
a
2
D
im
a
g
e,
we
f
ir
s
t
d
ef
in
e
a
win
d
o
w
s
ize
w.
T
h
is
win
d
o
w
s
ize
d
eter
m
in
es
th
e
d
im
en
s
io
n
s
o
f
th
e
r
esu
ltin
g
im
a
g
e,
wh
e
r
e
ea
c
h
p
ix
el
in
te
n
s
ity
co
r
r
esp
o
n
d
s
to
t
h
e
av
er
ag
e
h
y
d
r
o
p
h
o
b
icity
v
alu
e
with
in
th
e
win
d
o
w.
Fo
r
a
n
im
ag
e
o
f
d
im
en
s
io
n
m
×m
,
we
r
esh
ap
ed
th
e
s
eq
u
en
ce
in
to
a
m
atr
ix
f
o
r
m
a
t.
T
h
e
v
alu
e
o
f
m
is
ch
o
s
en
b
ased
o
n
th
e
s
eq
u
en
ce
le
n
g
th
n
an
d
t
h
e
d
esire
d
r
eso
lu
tio
n
.
T
h
e
tr
an
s
f
o
r
m
atio
n
p
r
o
ce
s
s
in
v
o
lv
es th
e
f
o
llo
win
g
s
tep
s
:
-
W
in
d
o
win
g
:
d
iv
i
d
e
th
e
h
y
d
r
o
p
h
o
b
icity
s
eq
u
en
ce
H
in
t
o
o
v
er
lap
p
in
g
o
r
n
o
n
-
o
v
er
lap
p
in
g
win
d
o
ws
o
f
s
ize
.
Fo
r
=
1
,
2
,
…
,
⌈
2
⌉
,
ea
ch
win
d
o
w
is
d
ef
in
e
d
as in
(
3
)
:
=
{
(
−
1
)
+
1
,
(
−
1
)
+
2
,
…
,
}
(
3
)
-
Av
er
ag
in
g
:
c
alcu
late
th
e
av
er
ag
e
h
y
d
r
o
p
h
o
b
icity
v
alu
e
f
o
r
ea
ch
win
d
o
w
.
T
h
e
av
er
a
g
e
h
y
d
r
o
p
h
o
b
icity
v
alu
e
̅
f
o
r
th
e
win
d
o
w
is
g
iv
en
b
y
(
4
)
:
̅
=
1
∑
=
(
−
1
)
+
1
(
4
)
-
Ma
tr
ix
f
o
r
m
atio
n
:
a
r
r
an
g
e
th
e
av
er
ag
e
h
y
d
r
o
p
h
o
b
icity
v
alu
e
s
̅
in
to
a
m
atr
ix
o
f
d
im
en
s
io
n
×
.
T
h
e
m
atr
ix
is
f
o
r
m
ed
in
(
5
)
as f
o
llo
ws
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Hyd
r
o
p
h
o
b
icity
s
ig
n
a
l a
n
a
lysi
s
fo
r
r
o
b
u
s
t S
A
R
S
-
C
o
V
-
2
cla
s
s
ifica
tio
n
(
Mo
h
a
mma
d
J
a
mh
u
r
i)
1297
=
(
̅
1
̅
2
…
̅
̅
+
1
̅
+
2
…
̅
2
⋮
⋮
⋱
⋮
̅
(
−
1
)
+
1
̅
(
−
1
)
+
2
…
̅
2
)
(
5
)
-
No
r
m
aliza
tio
n
:
n
o
r
m
alize
th
e
m
atr
ix
M
to
en
s
u
r
e
th
e
p
ix
el
v
alu
es
ar
e
with
in
a
s
p
ec
if
ic
r
an
g
e
(
e.
g
.
,
0
to
2
5
5
f
o
r
g
r
ay
s
ca
le
im
ag
es).
T
h
e
n
o
r
m
alize
d
m
atr
ix
M
is
g
iv
en
b
y
(
6
)
:
′
=
255
×
−
m
i
n
(
)
m
ax
(
)
−
m
i
n
(
)
.
(
6
)
-
I
m
ag
e
r
ep
r
esen
tatio
n
:
t
h
e
n
o
r
m
alize
d
m
atr
ix
M'
is
th
en
co
n
v
er
ted
in
to
a
g
r
ay
s
ca
le
im
ag
e,
wh
er
e
ea
ch
p
ix
el
in
ten
s
ity
r
ep
r
esen
ts
th
e
h
y
d
r
o
p
h
o
b
icity
v
alu
e
o
f
th
e
co
r
r
esp
o
n
d
in
g
win
d
o
w
in
th
e
s
eq
u
e
n
c
e.
-
R
em
o
v
e
n
o
n
-
ess
en
tial
g
r
ap
h
ical
elem
en
ts
:
t
o
p
r
ep
ar
e
clea
n
im
ag
es,
lab
els,
titl
es,
leg
en
d
s
,
an
d
ax
es
wer
e
r
em
o
v
ed
.
B
y
tr
an
s
f
o
r
m
in
g
t
h
e
am
in
o
ac
id
s
eq
u
en
ce
s
i
n
to
h
y
d
r
o
p
h
o
b
i
city
-
b
ased
im
ag
es
f
o
llo
win
g
(
2
)
-
(
6
)
,
w
e
en
ab
le
th
e
C
NN
to
ef
f
ec
tiv
el
y
ca
p
tu
r
e
th
e
s
p
atial
p
atter
n
s
a
n
d
ch
a
r
ac
ter
is
tics
in
h
er
en
t
i
n
th
e
h
y
d
r
o
p
h
o
b
icity
p
r
o
f
iles
.
T
h
is
tr
an
s
f
o
r
m
atio
n
en
h
an
ce
d
th
e
m
o
d
el'
s
ab
ilit
y
to
class
if
y
SA
R
S
-
C
o
V
-
2
an
d
n
o
n
-
SAR
S
-
C
o
V
-
2
v
ir
u
s
es.
Fig
u
r
e
1
illu
s
tr
ates th
e
p
r
o
ce
s
s
o
f
g
e
n
er
atin
g
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als f
r
o
m
DNA
s
eq
u
en
ce
s
.
Fig
u
r
e
1
.
Pre
p
r
o
ce
s
s
in
g
wo
r
k
f
lo
w:
DNA
s
eq
u
en
ce
to
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al
co
n
v
er
s
io
n
2
.
3
.
M
o
del im
plem
ent
a
t
io
n
W
e
im
p
lem
en
ted
a
co
n
v
o
lu
ti
o
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
to
p
r
o
ce
s
s
im
ag
e
-
b
ased
h
y
d
r
o
p
h
o
b
icit
y
p
r
o
f
iles
.
T
h
e
C
NN
ar
ch
itectu
r
e
in
clu
d
es
m
u
ltip
le
co
n
v
o
lu
t
io
n
al,
m
ax
-
p
o
o
lin
g
,
a
n
d
f
u
lly
co
n
n
ec
ted
lay
er
s
.
T
h
is
s
tr
u
ctu
r
e
allo
ws
th
e
m
o
d
el
to
ca
p
tu
r
e
co
m
p
lex
p
atter
n
s
with
in
t
h
e
h
y
d
r
o
p
h
o
b
icity
im
ag
es,
im
p
r
o
v
in
g
its
p
er
f
o
r
m
an
ce
i
n
th
e
b
in
ar
y
cla
s
s
if
icatio
n
.
T
h
e
n
etwo
r
k
b
eg
i
n
s
with
in
p
u
t
im
ag
es
o
f
h
y
d
r
o
p
h
o
b
icity
p
r
o
f
iles
,
wh
ich
ar
e
p
r
o
ce
s
s
ed
th
r
o
u
g
h
co
n
v
o
l
u
tio
n
al
an
d
p
o
o
lin
g
la
y
er
s
to
ex
tr
ac
t
r
ele
v
an
t
f
ea
tu
r
es.
T
h
ese
ex
tr
ac
ted
f
ea
tu
r
es
ar
e
p
ass
ed
th
r
o
u
g
h
f
u
lly
co
n
n
ec
te
d
lay
er
s
to
g
e
n
er
ate
th
e
f
in
al
b
in
ar
y
class
if
icatio
n
o
u
tp
u
t.
T
h
e
ar
ch
itectu
r
e
o
f
th
e
C
NN
is
d
ep
icted
in
Fig
u
r
e
2
.
T
ab
le
2
p
r
o
v
id
es
th
e
d
etails
o
f
C
NN
ar
ch
itectu
r
e.
E
ac
h
lay
er
'
s
ty
p
e,
th
e
n
u
m
b
er
o
f
f
ilter
s
o
r
u
n
its
,
k
er
n
el
s
ize
,
ac
tiv
atio
n
f
u
n
ctio
n
,
a
n
d
o
u
tp
u
t
s
h
ap
e
ar
e
s
p
e
cif
ied
to
g
iv
e
a
c
o
m
p
r
eh
en
s
i
v
e
o
v
e
r
v
iew
o
f
th
e
n
etwo
r
k
s
tr
u
ct
u
r
e.
T
h
is
d
etaile
d
b
r
ea
k
d
o
w
n
h
el
p
s
u
s
u
n
d
er
s
t
an
d
th
e
f
lo
w
o
f
d
ata
th
r
o
u
g
h
t
h
e
n
etwo
r
k
a
n
d
th
e
tr
an
s
f
o
r
m
atio
n
s
ap
p
lied
at
ea
c
h
s
tag
e.
I
n
th
e
f
ir
s
t
la
y
er
,
a
c
o
n
v
o
lu
ti
o
n
al
o
p
er
atio
n
is
a
p
p
lied
with
3
2
f
ilter
s
o
f
s
ize
3
×
3
,
p
r
o
d
u
cin
g
an
o
u
tp
u
t
s
h
a
p
e
o
f
(
1
2
8
,
1
2
8
,
3
2
)
.
T
h
is
co
n
v
o
lu
tio
n
al
lay
e
r
is
f
o
llo
wed
b
y
a
m
ax
-
p
o
o
lin
g
lay
er
with
a
2
×
2
f
ilter
s
ize,
wh
ich
r
ed
u
ce
s
th
e
s
p
atial
d
im
en
s
io
n
s
to
(
6
4
,
6
4
,
3
2
)
.
T
h
e
s
ec
o
n
d
co
n
v
o
lu
tio
n
al
lay
er
u
s
es
6
4
f
ilter
s
o
f
s
ize
3
×
3
,
r
esu
ltin
g
in
an
o
u
tp
u
t
s
h
ap
e
o
f
(
6
4
,
6
4
,
6
4
)
.
T
h
is
lay
er
is
th
en
f
o
llo
wed
b
y
an
o
th
er
m
ax
-
p
o
o
lin
g
lay
er
,
r
e
d
u
cin
g
th
e
d
im
en
s
io
n
s
to
(
3
2
,
3
2
,
6
4
)
.
T
h
e
th
ir
d
co
n
v
o
lu
tio
n
al
lay
er
ap
p
lies
1
2
8
f
ilter
s
o
f
s
ize
3
×
3
,
p
r
o
d
u
cin
g
an
o
u
tp
u
t
s
h
ap
e
o
f
(
3
2
,
3
2
,
1
2
8
)
.
I
t
is
f
o
llo
wed
b
y
a
m
ax
-
p
o
o
lin
g
lay
er
,
wh
ic
h
f
u
r
th
er
r
ed
u
ce
s
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
2
,
Feb
r
u
a
r
y
20
25
:
1
2
9
4
-
1
3
0
5
1298
d
im
en
s
io
n
s
to
(
1
6
,
1
6
,
1
2
8
)
.
T
h
ese
co
n
v
o
lu
tio
n
al
an
d
p
o
o
l
in
g
lay
e
r
s
ar
e
d
esig
n
ed
to
ex
t
r
ac
t
an
d
co
n
d
en
s
e
f
ea
tu
r
es
p
r
o
g
r
ess
iv
ely
,
ca
p
tu
r
i
n
g
th
e
ess
en
tial
p
atter
n
s
in
th
e
h
y
d
r
o
p
h
o
b
icity
p
r
o
f
iles
.
T
h
e
o
u
tp
u
t
o
f
th
e
f
in
al
p
o
o
lin
g
la
y
er
is
th
en
f
latten
e
d
an
d
f
e
d
in
to
a
f
u
lly
co
n
n
ec
ted
d
en
s
e
lay
er
with
5
1
2
u
n
its
,
wh
ich
p
r
o
v
i
d
es
a
r
ich
r
ep
r
esen
tatio
n
o
f
th
e
ex
tr
ac
ted
f
ea
tu
r
es.
T
h
is
d
e
n
s
e
lay
er
is
cr
u
cial
f
o
r
in
teg
r
atin
g
th
e
f
ea
tu
r
es
o
b
tain
e
d
b
y
th
e
p
r
ev
io
u
s
co
n
v
o
lu
tio
n
al
lay
er
s
an
d
p
r
ep
ar
in
g
th
e
m
f
o
r
class
if
icatio
n
.
T
h
e
f
in
al
lay
e
r
is
an
o
th
er
d
en
s
e
lay
er
eq
u
ip
p
ed
with
a
s
ig
m
o
i
d
ac
tiv
atio
n
f
u
n
ctio
n
,
wh
ich
p
r
o
d
u
ce
s
a
p
r
o
b
ab
ilit
y
f
o
r
th
e
b
in
ar
y
class
i
f
icatio
n
(
SAR
S
-
C
o
V
-
2
v
s
.
n
o
n
-
SAR
S
-
C
o
V
-
2
)
.
T
h
e
o
u
tp
u
t
s
h
ap
e
o
f
t
h
is
f
in
al
lay
er
is
(
1
)
,
c
o
r
r
esp
o
n
d
in
g
t
o
th
e
b
in
ar
y
class
if
icatio
n
task
.
T
h
is
s
etu
p
allo
ws
th
e
m
o
d
el
to
o
u
tp
u
t
a
p
r
o
b
ab
ilit
y
f
o
r
th
e
p
o
s
itiv
e
class
,
f
ac
ili
tatin
g
ac
cu
r
ate
class
if
icatio
n
.
T
h
e
C
N
N
wa
s
im
p
lem
en
ted
u
s
in
g
th
e
T
en
s
o
r
Flo
w
an
d
Ker
as
lib
r
ar
ies,
allo
win
g
ef
f
icien
t m
o
d
el
t
r
ain
in
g
a
n
d
e
v
alu
atio
n
.
Fig
u
r
e
2
.
T
h
e
ar
ch
itectu
r
e
o
f
t
h
e
C
NN
m
o
d
el:
lay
er
co
n
f
ig
u
r
atio
n
an
d
s
eq
u
en
ce
T
ab
le
2
.
C
NN
m
o
d
el
s
p
ec
if
ica
tio
n
La
y
e
r
Ty
p
e
N
u
mb
e
r
o
f
f
i
l
t
e
r
s
K
e
r
n
e
l
si
z
e
O
u
t
p
u
t
s
h
a
p
e
1
C
o
n
v
o
l
u
t
i
o
n
32
3
×
3
(
1
2
8
,
1
2
8
,
32)
2
M
a
x
p
o
o
l
i
n
g
-
2
×
2
(
6
4
,
6
4
,
3
2
)
3
C
o
n
v
o
l
u
t
i
o
n
64
3
×
3
(
6
4
,
6
4
,
6
4
)
4
M
a
x
p
o
o
l
i
n
g
-
2
×
2
(
3
2
,
3
2
,
6
4
)
5
C
o
n
v
o
l
u
t
i
o
n
1
2
8
3
×
3
(
3
2
,
3
2
,
1
2
8
)
6
M
a
x
p
o
o
l
i
n
g
-
2
×
2
(
1
6
,
1
6
,
1
2
8
)
7
F
u
l
l
y
c
o
n
n
e
c
t
e
d
(
d
e
n
s
e
)
5
1
2
-
(
5
1
2
)
8
O
u
t
p
u
t
(
d
e
n
se,
si
g
m
o
i
d
)
1
-
(
1
)
W
e
in
clu
d
e
a
v
ar
iety
o
f
b
as
elin
e
m
ac
h
in
e
lear
n
in
g
m
o
d
e
ls
im
p
lem
en
ted
u
s
in
g
th
e
s
c
i
-
k
it
-
lear
n
lib
r
ar
y
,
wh
ich
is
c
o
m
p
a
r
ed
a
g
ain
s
t
th
e
C
NN
m
o
d
el.
T
h
ese
m
o
d
els
a
r
e
e
x
p
licitly
co
n
f
i
g
u
r
ed
f
o
r
h
an
d
lin
g
DNA
s
eq
u
en
ce
d
ata
as f
o
llo
w
s
:
-
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SV
M)
:
u
tili
ze
s
a
lin
ea
r
k
er
n
el
to
class
if
y
lin
ea
r
ly
s
ep
ar
ab
le
d
ata.
No
n
-
lin
ea
r
class
if
icatio
n
is
en
ab
led
u
s
in
g
k
er
n
el
tr
ick
s
,
with
th
e
r
eg
u
lar
izatio
n
p
ar
a
m
eter
s
et
to
1
.
0
,
b
ala
n
c
in
g
th
e
class
if
icatio
n
m
ar
g
in
with
er
r
o
r
m
in
im
izatio
n
[
2
2
]
.
-
K
-
n
ea
r
est
n
eig
h
b
o
r
s
(K
-
NN)
:
e
m
p
lo
y
a
d
is
tan
ce
m
etr
ic
(
E
u
clid
ea
n
m
etr
ic)
to
id
en
tify
th
e
clo
s
est
tr
ain
in
g
ex
am
p
les
an
d
d
eter
m
in
e
th
e
class
if
icatio
n
[
2
3
]
.
Set
th
e
n
u
m
b
er
o
f
n
eig
h
b
o
r
s
as
3
to
e
n
s
u
r
e
p
r
o
x
im
ity
af
f
ec
ts
th
e
class
if
icatio
n
d
ec
is
io
n
.
-
L
o
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
:
p
r
e
d
icts
ca
teg
o
r
ical
o
u
tco
m
es
u
s
in
g
a
lo
g
is
tic
f
u
n
ctio
n
,
m
o
d
e
lin
g
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
s
[
2
4
]
.
I
t w
as c
o
n
f
ig
u
r
ed
with
a
'
lin
ea
r
'
s
o
lv
er
,
a
r
eg
u
lar
izatio
n
s
tr
en
g
th
o
f
1
,
an
d
an
L
2
p
en
alty
to
m
itig
ate
o
v
er
f
itti
n
g
.
-
Dec
is
io
n
t
r
ee
(
DT
)
:
t
h
is
m
o
d
el
class
if
ie
s
d
ata
b
y
cr
ea
tin
g
a
tr
ee
th
at
m
o
d
els
d
ec
is
io
n
s
b
ased
o
n
f
ea
tu
r
e
v
alu
es.
I
t
is
co
n
f
ig
u
r
e
d
with
o
u
t
a
m
ax
im
u
m
d
ep
th
to
allo
w
d
etailed
tr
ee
g
r
o
wth
an
d
u
s
es
a
m
in
im
u
m
s
am
p
le
s
p
lit o
f
2
[
2
5
]
.
-
R
an
d
o
m
f
o
r
est
(
R
F):
t
h
is
en
s
em
b
le
m
o
d
el
b
u
ild
s
m
u
ltip
l
e
d
ec
is
io
n
tr
ee
s
a
n
d
m
er
g
es
th
eir
o
u
tp
u
t
to
im
p
r
o
v
e
ac
c
u
r
ac
y
an
d
c
o
n
tr
o
l
o
v
er
f
itti
n
g
.
I
t
u
s
ed
1
0
0
e
s
tim
ato
r
s
an
d
th
e
'
s
q
r
t'
m
et
h
o
d
f
o
r
f
ea
t
u
r
e
s
elec
tio
n
,
o
p
tim
izin
g
v
ar
ian
ce
r
ed
u
ctio
n
a
n
d
p
r
ed
ictiv
e
ac
c
u
r
ac
y
[
2
6
]
.
-
XGBo
o
s
t:
a
n
im
p
lem
en
tatio
n
o
f
g
r
ad
ien
t
-
b
o
o
s
ted
d
ec
is
io
n
tr
ee
s
d
esig
n
ed
f
o
r
s
p
ee
d
a
n
d
p
er
f
o
r
m
an
ce
,
co
n
f
ig
u
r
ed
with
1
0
0
esti
m
ato
r
s
,
a
m
ax
im
u
m
d
e
p
th
o
f
3
,
a
n
d
a
lear
n
in
g
r
ate
0
.
1
.
Gr
id
s
ea
r
ch
is
u
s
ed
to
f
in
e
-
tu
n
e
p
ar
am
eter
s
b
ased
o
n
p
er
f
o
r
m
an
ce
[
2
7
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Hyd
r
o
p
h
o
b
icity
s
ig
n
a
l a
n
a
lysi
s
fo
r
r
o
b
u
s
t S
A
R
S
-
C
o
V
-
2
cla
s
s
ifica
tio
n
(
Mo
h
a
mma
d
J
a
mh
u
r
i)
1299
-
Mu
ltil
ay
er
p
er
ce
p
tr
o
n
(
ML
P):
a
n
eu
r
al
n
etwo
r
k
m
o
d
el
co
n
s
is
tin
g
o
f
m
u
ltip
le
lay
er
s
o
f
n
o
d
e
s
,
in
clu
d
in
g
an
in
p
u
t la
y
er
,
o
n
e
o
r
m
o
r
e
h
id
d
e
n
lay
er
s
,
an
d
an
o
u
tp
u
t la
y
er
[
2
8
]
.
I
t w
as tu
n
ed
with
a
m
ax
im
u
m
iter
atio
n
o
f
1
0
0
to
en
s
u
r
e
co
n
v
er
g
en
ce
a
n
d
o
p
tim
ized
f
o
r
b
in
ar
y
class
if
icatio
n
task
s
.
2
.
4
.
E
x
perim
ent
a
l set
up
T
h
is
p
h
ase
o
u
tlin
es
th
e
ex
p
e
r
i
m
en
tal
s
etu
p
u
s
ed
to
ev
al
u
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
C
N
N
m
o
d
el
ag
ai
n
s
t
v
ar
io
u
s
b
aselin
e
m
o
d
els.
W
e
ass
es
s
ed
m
o
d
el
p
er
f
o
r
m
an
ce
u
s
in
g
k
ey
m
etr
ics:
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e
,
a
n
d
AUC
-
R
OC
.
Ad
d
itio
n
ally
,
we
m
ea
s
u
r
ed
th
e
c
o
m
p
u
tatio
n
tim
e
r
eq
u
ir
e
d
f
o
r
tr
ain
in
g
a
n
d
p
r
ed
ictio
n
to
e
v
alu
ate
th
e
p
r
ac
ticality
an
d
ef
f
icien
cy
o
f
t
h
e
m
eth
o
d
.
T
h
e
ex
p
e
r
im
en
t
was
s
tr
u
ctu
r
ed
as f
o
llo
ws:
-
C
o
m
p
ar
is
o
n
with
b
aselin
e
m
eth
o
d
s
u
s
in
g
m
atch
in
g
s
eq
u
en
ce
len
g
th
s
: e
ac
h
m
o
d
el
was tr
ain
ed
o
n
th
e
s
am
e
d
ataset
an
d
d
iv
id
ed
in
to
tr
a
in
in
g
,
v
alid
atio
n
,
a
n
d
test
in
g
s
ets
in
a
7
0
-
15
-
1
5
r
atio
.
Un
if
o
r
m
d
ata
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
,
s
u
ch
as n
o
r
m
aliza
tio
n
an
d
en
c
o
d
in
g
,
wer
e
ap
p
lied
b
ef
o
r
e
tr
ain
in
g
.
-
C
o
m
p
ar
is
o
n
with
b
aselin
e
m
e
th
o
d
s
u
s
in
g
ex
ten
d
ed
s
eq
u
en
c
e
len
g
th
s
:
test
d
ata
in
clu
d
e
d
s
eq
u
en
ce
len
g
th
s
ex
ce
ed
in
g
th
e
m
ax
im
u
m
s
eq
u
en
ce
len
g
th
o
f
th
e
tr
ain
in
g
d
a
ta
to
test
th
e
m
o
d
els'
s
tab
ilit
y
an
d
r
o
b
u
s
tn
ess
with
lo
n
g
er
s
eq
u
en
ce
s
.
-
Ass
es
s
m
en
t
o
f
co
m
p
u
tatio
n
ti
m
e:
to
e
v
alu
ate
th
e
m
eth
o
d
'
s
p
r
ac
ticality
an
d
ef
f
icien
c
y
,
w
e
m
ea
s
u
r
ed
th
e
tim
e
r
eq
u
ir
ed
to
tr
ain
t
h
e
m
o
d
el,
p
r
o
ce
s
s
r
aw
s
eq
u
en
ce
s
in
to
im
ag
es,
an
d
p
r
ed
ict
n
ew
d
ata.
-
C
o
n
f
u
s
io
n
m
atr
ix
an
d
AUC
-
R
OC
an
aly
s
is
:
we
an
aly
ze
d
t
h
e
co
n
f
u
s
io
n
m
atr
i
x
an
d
R
OC
cu
r
v
e
wit
h
th
e
AUC
s
co
r
e
to
v
is
u
alize
tr
u
e
p
o
s
itiv
es,
f
alse
p
o
s
itiv
es,
an
d
f
alse
n
eg
ativ
es,
p
r
o
v
id
i
n
g
in
s
ig
h
t
in
to
class
if
icatio
n
ac
cu
r
ac
y
an
d
t
h
e
m
o
d
el'
s
d
is
cr
im
in
ativ
e
ab
ilit
y
.
W
e
em
p
lo
y
ed
a
v
alid
atio
n
s
tr
ateg
y
to
en
h
an
ce
m
o
d
el
r
o
b
u
s
tn
ess
an
d
r
eliab
ilit
y
,
in
clu
d
i
n
g
u
s
in
g
a
v
alid
atio
n
s
et
to
f
in
e
-
tu
n
e
t
h
e
m
o
d
els
an
d
p
r
ev
en
t
o
v
e
r
f
itti
n
g
.
Ad
d
itio
n
ally
,
we
u
til
ized
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
,
d
iv
id
i
n
g
th
e
tr
ain
i
n
g
d
ataset
in
to
ten
s
u
b
s
ets.
T
h
e
m
o
d
el
was
tr
ain
ed
ten
tim
es,
ea
ch
iter
atio
n
u
s
in
g
a
d
if
f
e
r
en
t su
b
s
et
f
o
r
v
al
i
d
atio
n
,
en
s
u
r
i
n
g
en
h
an
ce
d
g
e
n
er
aliza
b
ilit
y
.
2
.
4
.
1
.
Co
m
pu
t
a
t
i
o
na
l r
eso
urce
s
I
n
th
e
e
x
p
er
im
e
n
ts
,
we
u
s
ed
an
I
n
tel
Xe
o
n
C
PU,
an
NV
I
DI
A
T
esla
P1
0
0
GPU
with
1
6
GB
o
f
m
em
o
r
y
,
an
d
2
9
GB
o
f
R
AM
.
W
e
co
n
d
u
cted
all
ex
p
e
r
im
en
ts
u
s
in
g
Kag
g
le
No
teb
o
o
k
s
wi
th
Py
th
o
n
3
.
7
.
W
e
u
s
ed
T
en
s
o
r
Flo
w
2
.
1
,
s
cik
it
-
lear
n
,
N
u
m
Py
,
a
n
d
Pan
d
as
f
o
r
m
ac
h
i
n
e
lear
n
in
g
.
T
h
is
s
etu
p
h
an
d
led
th
e
co
m
p
u
tatio
n
al
d
em
an
d
s
o
f
p
r
o
ce
s
s
in
g
lar
g
e
d
atasets
an
d
t
r
ain
in
g
co
m
p
lex
m
o
d
els
s
u
c
h
as
C
NN.
Kag
g
le
p
r
o
v
id
e
d
a
s
tab
le
p
latf
o
r
m
f
o
r
r
eliab
le
ex
ec
u
tio
n
,
a
n
d
t
h
e
h
ig
h
-
p
er
f
o
r
m
an
ce
GPU
ef
f
icien
tly
p
r
o
ce
s
s
ed
co
m
p
u
tatio
n
ally
h
ea
v
y
task
s
,
s
u
ch
as a
n
aly
zin
g
DNA
s
eq
u
en
ce
s
.
2
.
4
.
2
.
Da
t
a
a
v
a
ila
bil
it
y
W
e
h
av
e
m
ad
e
th
e
d
ataset
u
s
e
d
in
th
is
s
tu
d
y
av
ailab
le
to
th
e
p
u
b
lic
o
n
Kag
g
le
t
o
e
n
s
u
r
e
ac
ce
s
s
ib
ilit
y
f
o
r
f
u
r
t
h
er
r
esear
ch
.
T
o
ac
ce
s
s
th
e
r
aw
d
ata,
p
lease
v
is
it
r
aw
-
d
ataset.
Fo
r
d
ata
tr
an
s
f
o
r
m
ed
in
t
o
a
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al,
p
lease
r
ef
er
to
th
e
I
m
ag
e
-
d
ataset.
T
h
ese
d
atasets
ar
e
p
r
o
v
id
ed
u
n
d
er
an
o
p
en
-
ac
ce
s
s
licen
s
e,
en
co
u
r
a
g
in
g
f
u
r
th
er
r
e
s
ea
r
ch
an
d
v
alid
atio
n
o
f
t
h
e
f
i
n
d
in
g
s
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
we
p
r
esen
ted
an
d
o
r
g
an
ized
th
e
e
x
p
er
im
e
n
t
al
r
esu
lts
in
to
f
iv
e
p
ar
ts
:
i
)
a
co
m
p
ar
is
o
n
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
with
b
aselin
e
m
eth
o
d
s
u
s
in
g
test
d
ata
with
s
eq
u
en
ce
len
g
th
s
m
atch
in
g
th
o
s
e
in
th
e
tr
ain
in
g
d
a
ta,
ii
)
an
ev
al
u
atio
n
o
f
th
e
m
eth
o
d
ag
ain
s
t
b
aselin
e
m
eth
o
d
s
u
s
in
g
test
d
ata
with
s
eq
u
en
ce
len
g
th
s
ex
ce
ed
in
g
t
h
e
m
ax
im
u
m
len
g
th
o
f
th
e
t
r
ain
in
g
d
ata,
iii
)
an
ass
es
s
m
en
t
o
f
th
e
co
m
p
u
tati
o
n
tim
e
r
eq
u
ir
ed
t
o
tr
ain
th
e
m
o
d
el,
p
r
o
ce
s
s
r
aw
s
eq
u
en
ce
s
in
to
im
ag
es,
an
d
m
a
k
e
p
r
ed
ictio
n
s
o
n
n
ew
d
ata,
iv
)
an
an
aly
s
is
o
f
th
e
p
r
ed
ictio
n
r
esu
lts
u
s
in
g
a
co
n
f
u
s
io
n
m
atr
ix
an
d
AUC
-
R
OC
s
co
r
e,
an
d
v
)
a
co
m
p
ar
is
o
n
with
p
r
ev
io
u
s
s
tu
d
ies.
T
h
e
o
b
jectiv
e
is
to
e
v
alu
ate
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
u
s
in
g
v
ar
io
u
s
m
et
r
ics
u
n
d
er
d
if
f
er
en
t
co
n
d
itio
n
s
an
d
to
ass
ess
th
e
p
r
ac
ticality
an
d
ef
f
icien
cy
o
f
t
h
e
m
eth
o
d
i
n
r
ea
l
-
wo
r
l
d
ap
p
licat
io
n
s
.
3
.
1
.
Co
m
pa
riso
n wit
h ba
s
eline
m
o
dels
us
ing
m
a
t
ching
s
equence
leng
t
h
s
W
e
co
m
p
ar
ed
th
e
p
r
o
p
o
s
ed
m
o
d
el'
s
p
er
f
o
r
m
a
n
ce
with
v
a
r
io
u
s
b
aselin
e
m
eth
o
d
s
u
s
in
g
test
d
ata
wh
er
e
th
e
s
eq
u
en
ce
len
g
th
s
m
atch
ed
t
h
o
s
e
in
th
e
tr
ain
in
g
d
ata.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
e
m
p
lo
y
s
a
C
NN
with
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als
as
f
ea
tu
r
es,
wh
ile
th
e
b
aselin
e
m
eth
o
d
s
u
s
e
r
aw
s
eq
u
en
ce
s
.
T
o
e
v
alu
ate
th
e
ef
f
ec
tiv
en
ess
o
f
ea
ch
m
o
d
el,
we
f
o
cu
s
ed
o
n
k
e
y
m
etr
ics
s
u
ch
as
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
e
ca
ll,
F1
-
s
co
r
e,
an
d
AUC
-
R
O
C
.
T
ab
le
3
p
r
o
v
id
es
a
co
m
p
ar
ativ
e
p
er
f
o
r
m
a
n
ce
s
u
m
m
ar
y
o
f
class
if
icatio
n
m
o
d
e
ls
u
s
in
g
th
ese
two
ty
p
es o
f
f
ea
tu
r
es.
T
h
e
r
esu
lts
in
d
icate
th
at
t
h
e
b
aselin
e
m
eth
o
d
s
p
er
f
o
r
m
ed
ex
ce
p
tio
n
ally
well,
ac
h
iev
in
g
av
er
a
g
e
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
F
1
-
s
co
r
e,
a
n
d
AUC
-
R
O
C
o
f
9
8
.
9
1
%,
9
8
.
2
8
%,
9
9
.
5
2
%,
9
8
.
8
9
%,
an
d
9
9
.
7
1
%,
r
esp
ec
tiv
ely
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
s
ig
n
if
ican
tly
im
p
r
o
v
ed
o
v
er
th
e
b
aselin
e
m
eth
o
d
s
,
with
an
in
cr
ea
s
e
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
2
,
Feb
r
u
a
r
y
20
25
:
1
2
9
4
-
1
3
0
5
1300
ap
p
r
o
x
im
ately
0
.
8
%
ac
r
o
s
s
all
m
etr
ics.
I
n
th
is
s
ce
n
ar
io
,
all
m
eth
o
d
s
ef
f
ec
tiv
ely
cl
ass
if
ied
th
e
d
ata,
m
ain
tain
in
g
an
av
er
a
g
e
er
r
o
r
r
ate
o
f
less
th
an
0
.
1
%.
T
ab
le
3
.
Per
f
o
r
m
an
ce
s
u
m
m
ar
y
o
f
class
if
ier
s
with
test
s
eq
u
en
ce
len
g
th
s
with
in
th
e
t
r
ain
in
g
r
an
g
e
C
l
a
s
si
f
i
e
r
F
e
a
t
u
r
e
Ty
p
e
A
c
c
.
P
r
e
c
.
R
e
c
.
F1
AUC
-
R
O
C
S
V
M
D
N
A
S
e
q
u
e
n
c
e
0
.
9
9
1
0
0
.
9
8
1
9
1
.
0
0
0
0
0
.
9
9
0
9
0
.
9
9
9
8
K
N
N
D
N
A
S
e
q
u
e
n
c
e
0
.
9
8
3
6
0
.
9
8
1
7
0
.
9
8
4
9
0
.
9
8
3
3
0
.
9
9
5
4
LR
D
N
A
S
e
q
u
e
n
c
e
0
.
9
9
0
2
0
.
9
8
3
5
0
.
9
9
6
7
0
.
9
9
0
0
0
.
9
9
8
2
DT
D
N
A
S
e
q
u
e
n
c
e
0
.
9
8
6
9
0
.
9
8
5
0
0
.
9
8
8
3
0
.
9
8
6
6
0
.
9
8
6
9
RF
D
N
A
S
e
q
u
e
n
c
e
0
.
9
8
9
4
0
.
9
7
8
7
1
.
0
0
0
0
0
.
9
8
9
2
0
.
9
9
9
7
X
G
B
o
o
st
D
N
A
S
e
q
u
e
n
c
e
0
.
9
9
1
0
0
.
9
8
1
9
1
.
0
0
0
0
0
.
9
9
0
9
0
.
9
9
9
7
M
LP
D
N
A
S
e
q
u
e
n
c
e
0
.
9
9
1
8
0
.
9
8
6
8
0
.
9
9
6
7
0
.
9
9
1
7
0
.
9
9
9
7
O
u
r
s
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
0
.
9
9
8
4
0
.
9
9
6
7
1
.
0
0
0
0
0
.
9
9
8
3
0
.
9
9
9
9
T
h
e
C
NN
m
o
d
el
ac
h
iev
ed
th
e
h
ig
h
est
ac
cu
r
ac
y
at
9
9
.
8
4
%,
i
n
d
icatin
g
its
s
u
p
er
i
o
r
a
b
ilit
y
t
o
co
r
r
ec
tly
class
if
y
p
o
s
itiv
e
an
d
n
eg
ativ
e
ca
s
es
co
m
p
ar
ed
to
th
e
b
aseli
n
e
m
o
d
els.
W
ith
a
h
ig
h
p
r
ec
is
io
n
o
f
9
9
.
6
7
%,
th
e
m
o
d
el
m
a
k
es
v
e
r
y
f
ew
f
alse
p
o
s
itiv
e
er
r
o
r
s
,
wh
ich
is
cr
u
cial
f
o
r
av
o
id
in
g
th
e
m
is
class
if
ica
tio
n
o
f
n
o
n
-
SAR
S
-
C
o
V
-
2
ca
s
es
as
SAR
S
-
C
o
V
-
2
.
T
h
e
m
o
d
el
also
ac
h
iev
e
d
p
er
f
ec
t
r
ec
all
at
1
0
0
%,
m
ea
n
in
g
it
co
r
r
ec
tly
id
en
tifie
s
all
ac
tu
al
p
o
s
itiv
e
ca
s
es,
en
s
u
r
in
g
n
o
SAR
S
-
C
o
V
-
2
ca
s
es
ar
e
m
is
s
ed
.
T
h
e
F
1
Sco
r
e
o
f
9
9
.
8
3
%
d
em
o
n
s
tr
ates
th
e
m
o
d
el'
s
o
v
er
all
ef
f
ec
tiv
en
ess
b
y
b
alan
cin
g
p
r
ec
is
io
n
a
n
d
r
ec
all.
Fu
r
th
er
m
o
r
e,
t
h
e
AUC
-
R
OC
s
co
r
e
o
f
0
.
9
9
9
9
illu
s
tr
ates
th
e
m
o
d
el'
s
ex
ce
llen
t
p
e
r
f
o
r
m
an
ce
in
d
is
tin
g
u
is
h
in
g
b
et
wee
n
class
es
ac
r
o
s
s
d
if
f
er
en
t th
r
esh
o
ld
v
alu
es,
h
ig
h
lig
h
tin
g
its
d
is
cr
im
in
ativ
e
p
o
wer
.
T
h
e
s
ig
n
if
ican
t
im
p
r
o
v
em
en
t
ac
r
o
s
s
all
m
etr
ics
h
ig
h
lig
h
ts
th
e
C
NN
m
o
d
el'
s
r
o
b
u
s
tn
ess
i
n
h
an
d
lin
g
th
e
v
ar
iab
ilit
y
an
d
co
m
p
le
x
ity
o
f
v
ir
al
g
e
n
etic
m
ater
ial.
T
h
e
u
s
e
o
f
a
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al
allo
ws
th
e
C
N
N
m
o
d
el
to
ca
p
t
u
r
e
i
n
tr
icate
p
a
tter
n
s
in
th
e
d
ata
th
at
t
r
a
d
itio
n
al
s
eq
u
e
n
ce
-
b
ased
m
eth
o
d
s
d
o
n
o
t
u
tili
ze
as
ef
f
ec
tiv
ely
.
T
h
e
h
ig
h
-
p
er
f
o
r
m
an
ce
m
etr
ics
s
u
g
g
est
th
at
th
e
C
NN
m
o
d
el
is
r
eliab
le
a
n
d
c
o
u
ld
b
e
u
s
ed
in
r
ea
l
-
wo
r
ld
ap
p
licatio
n
s
f
o
r
r
ap
i
d
an
d
ac
cu
r
ate
v
i
r
al
class
if
icatio
n
,
p
o
ten
tially
lead
in
g
to
b
etter
o
u
tb
r
ea
k
m
an
ag
em
en
t a
n
d
p
u
b
lic
h
ea
lth
r
esp
o
n
s
es.
3
.
2
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n wit
h e
x
t
ended seq
uence
len
g
t
hs
W
e
ev
alu
ated
th
e
r
o
b
u
s
tn
ess
a
n
d
ad
ap
tab
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
b
y
co
m
p
ar
i
n
g
it
with
b
aselin
e
m
eth
o
d
s
u
s
in
g
test
d
ata
with
s
eq
u
en
ce
len
g
th
s
ex
ce
ed
in
g
th
e
m
ax
im
u
m
len
g
th
o
f
th
e
tr
ain
in
g
d
ata.
T
h
e
an
aly
s
is
f
o
cu
s
ed
o
n
h
o
w
well
th
e
m
o
d
els
h
a
n
d
led
l
o
n
g
er
s
eq
u
en
ce
s
an
d
th
e
im
p
ac
t
o
n
p
e
r
f
o
r
m
a
n
ce
m
etr
ics.
A
two
-
p
h
ase
s
tu
d
y
was
co
n
d
u
cted
to
ass
ess
th
e
r
o
b
u
s
tn
ess
o
f
th
e
C
NN
cla
s
s
if
ier
u
s
in
g
h
y
d
r
o
p
h
o
b
ic
ity
s
ig
n
als.
I
n
t
h
e
f
ir
s
t
p
h
ase,
we
u
s
ed
r
aw
DNA
s
eq
u
en
ce
s
as
in
p
u
ts
f
o
r
b
aselin
e
class
if
ier
s
an
d
m
ea
s
u
r
ed
th
ei
r
p
er
f
o
r
m
an
ce
.
I
n
th
e
s
ec
o
n
d
p
h
ase,
we
u
s
ed
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als
as
in
p
u
ts
f
o
r
all
cla
s
s
if
ier
s
to
co
m
p
ar
e
th
eir
p
er
f
o
r
m
an
ce
d
ir
ec
tly
a
g
a
in
s
t th
e
b
aselin
e
m
eth
o
d
s
.
3
.
2
.
1
.
P
e
r
f
o
r
m
a
n
c
e
c
o
m
p
a
r
i
s
o
n
:
b
a
s
e
l
i
n
e
c
l
a
s
s
i
f
i
e
r
w
i
t
h
D
N
A
s
e
q
u
e
n
c
e
v
s
.
C
N
N
w
i
t
h
h
y
d
r
o
p
h
o
b
i
c
i
t
y
s
i
g
n
a
l
I
n
th
is
p
h
ase,
th
e
test
s
et
s
eq
u
en
ce
s
wer
e
lo
n
g
er
th
an
th
o
s
e
in
th
e
tr
ain
in
g
s
et.
T
h
is
s
ce
n
ar
i
o
aim
ed
to
d
eter
m
in
e
wh
et
h
er
th
e
clas
s
if
icatio
n
m
o
d
els
co
u
ld
p
r
e
d
ict
t
h
e
test
s
et
as
ef
f
ec
tiv
ely
as
in
th
e
f
ir
s
t
s
ce
n
ar
i
o
.
T
ab
le
4
p
r
esen
ts
a
p
er
f
o
r
m
a
n
c
e
co
m
p
ar
is
o
n
o
f
v
ar
io
u
s
class
if
icatio
n
m
o
d
els o
n
an
ex
te
n
d
e
d
test
s
et
wh
er
e
th
e
DNA
s
eq
u
en
ce
len
g
th
is
lo
n
g
e
r
th
an
th
at
o
f
th
e
tr
ai
n
in
g
s
et.
T
h
e
ex
p
er
im
e
n
tal
r
esu
lts
s
h
o
w
th
at
th
e
b
aselin
e
m
eth
o
d
s
ac
h
iev
ed
m
u
c
h
lo
wer
av
er
ag
e
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e,
an
d
AUC
-
R
O
C
co
m
p
ar
ed
t
o
th
e
f
ir
s
t
ex
p
er
im
e
n
t,
s
p
ec
if
ically
4
4
.
3
6
%,
9
1
.
2
3
%,
4
4
.
3
6
%,
4
9
.
4
0
%,
an
d
9
6
.
2
9
%,
r
esp
ec
tiv
ely
.
Me
a
n
wh
ile,
th
e
p
r
o
p
o
s
ed
m
eth
o
d
m
ain
tain
ed
h
ig
h
p
er
f
o
r
m
an
ce
,
ac
h
iev
in
g
m
o
r
e
th
a
n
9
9
.
7
3
%
f
o
r
all
m
etr
ics.
T
h
is
r
esu
lt
d
em
o
n
s
tr
ates
th
e
r
o
b
u
s
tn
ess
o
f
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
in
h
an
d
lin
g
d
if
f
er
en
ce
s
in
d
ata
s
am
p
le
s
ize
d
u
r
in
g
th
e
p
r
ed
ic
tio
n
p
h
ase,
e
v
en
wh
e
n
th
e
d
ata
s
ize
is
o
u
ts
i
d
e
th
e
r
an
g
e
o
f
th
e
tr
ain
in
g
s
et.
T
h
e
C
NN
m
o
d
el'
s
h
ig
h
p
er
f
o
r
m
an
ce
ca
n
b
e
attr
i
b
u
ted
t
o
i
ts
ab
ilit
y
to
ca
p
tu
r
e
in
tr
icate
p
atter
n
s
in
th
e
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al
d
er
iv
e
d
f
r
o
m
DNA
s
eq
u
en
ce
s
.
Un
lik
e
tr
ad
i
tio
n
al
m
eth
o
d
s
th
at
r
ely
o
n
d
ir
ec
t
s
eq
u
e
n
ce
a
n
a
ly
s
is
,
th
e
im
ag
e
-
b
ased
a
p
p
r
o
ac
h
lev
e
r
ag
es
th
e
s
p
atial
i
n
f
o
r
m
atio
n
in
th
e
h
y
d
r
o
p
h
o
b
icity
p
r
o
f
iles
,
allo
w
in
g
th
e
m
o
d
el
to
lear
n
co
m
p
le
x
f
ea
tu
r
es th
at
en
h
an
ce
class
if
icatio
n
ac
cu
r
ac
y
.
Fu
r
th
er
m
o
r
e
,
wh
ile
p
r
ec
is
io
n
an
d
AUC
-
R
OC
s
co
r
es
wer
e
h
ig
h
f
o
r
th
e
b
aselin
e
m
eth
o
d
s
,
e
x
ce
ed
in
g
9
0
%,
th
e
o
th
er
m
etr
ics
d
id
n
o
t
ex
ce
ed
5
0
%.
T
h
ese
e
x
p
er
im
en
tal
r
esu
lts
in
d
icate
th
at
th
e
b
aselin
e
m
o
d
els
ar
e
v
er
y
co
n
s
er
v
ativ
e
in
p
r
e
d
ictin
g
th
e
p
o
s
itiv
e
class
,
r
esu
ltin
g
in
h
ig
h
p
r
ec
is
io
n
.
T
h
ey
o
n
ly
p
r
ed
ict
p
o
s
itiv
es
wh
en
th
ey
ar
e
v
er
y
co
n
f
id
en
t
,
lead
in
g
to
f
ew
f
alse
p
o
s
itiv
es.
Ho
wev
er
,
th
is
c
o
n
s
er
v
atis
m
r
esu
lts
in
m
an
y
ac
tu
al
p
o
s
itiv
es b
ein
g
m
is
s
ed
(
h
ig
h
f
alse n
eg
ativ
es),
wh
ich
lo
wer
s
r
ec
all.
T
h
e
h
ig
h
AUC
-
R
OC
v
alu
e
in
d
icate
s
th
at
th
e
m
o
d
els
ar
e
g
o
o
d
at
d
is
tin
g
u
is
h
in
g
b
etwe
en
p
o
s
itiv
e
an
d
n
eg
ativ
e
class
es.
Sti
ll,
th
ey
s
tr
u
g
g
le
with
s
elec
tin
g
a
th
r
esh
o
ld
th
at
b
ala
n
ce
s
p
r
ec
is
io
n
an
d
r
ec
all.
T
h
e
lo
w
ac
cu
r
ac
y
s
u
g
g
ests
th
e
p
r
ed
ictio
n
im
b
alan
ce
lead
s
to
p
o
o
r
p
er
f
o
r
m
an
c
e
r
eg
ar
d
in
g
co
r
r
ec
t
class
if
icatio
n
s
.
I
n
s
u
m
m
ar
y
,
th
ese
m
et
r
ics
in
d
icate
th
at
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Hyd
r
o
p
h
o
b
icity
s
ig
n
a
l a
n
a
lysi
s
fo
r
r
o
b
u
s
t S
A
R
S
-
C
o
V
-
2
cla
s
s
ifica
tio
n
(
Mo
h
a
mma
d
J
a
mh
u
r
i)
1301
b
aselin
e
m
o
d
els
a
r
e
ca
u
ti
o
u
s
an
d
o
n
ly
p
r
e
d
ict
p
o
s
itiv
e
w
h
en
v
er
y
c
o
n
f
id
e
n
t,
lea
d
in
g
to
h
ig
h
p
r
ec
is
io
n
an
d
AUC
-
R
O
C
b
u
t
at
th
e
co
s
t
o
f
r
ec
all
an
d
o
v
er
all
ac
cu
r
a
cy
.
Ou
r
p
r
o
p
o
s
ed
m
eth
o
d
,
b
y
co
n
tr
ast,
s
h
o
ws
s
ig
n
if
ican
t
r
o
b
u
s
tn
ess
an
d
ad
ap
tab
ilit
y
,
m
ak
i
n
g
it
well
-
s
u
ite
d
f
o
r
r
ea
l
-
w
o
r
ld
ap
p
licatio
n
s
wh
er
e
s
eq
u
en
ce
len
g
th
s
ca
n
v
a
r
y
s
ig
n
if
ican
tly
.
T
ab
le
4
.
C
o
m
p
a
r
ativ
e
p
er
f
o
r
m
an
ce
m
etr
ics f
o
r
e
x
ten
d
e
d
test
s
ets with
lo
n
g
er
s
eq
u
en
ce
len
g
th
s
: b
aselin
e
DNA
s
eq
u
en
ce
v
s
.
C
NN
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al
C
l
a
s
si
f
i
e
r
F
e
a
t
u
r
e
Ty
p
e
A
c
c
.
Pr
ec.
R
e
c
.
F1
AUC
-
R
O
C
S
V
M
D
N
A
S
e
q
u
e
n
c
e
0
.
2
5
5
0
0
.
9
0
7
2
0
.
2
5
5
0
0
.
2
7
8
9
0
.
9
9
8
8
K
N
N
D
N
A
S
e
q
u
e
n
c
e
0
.
5
0
4
8
0
.
9
1
2
7
0
.
5
0
4
8
0
.
5
8
3
3
0
.
9
7
4
5
LR
D
N
A
S
e
q
u
e
n
c
e
0
.
5
7
2
3
0
.
9
1
5
0
0
.
5
7
2
3
0
.
6
4
8
0
0
.
9
4
6
1
DT
D
N
A
S
e
q
u
e
n
c
e
0
.
6
9
7
6
0
.
9
2
1
5
0
.
6
9
7
6
0
.
7
5
5
7
0
.
8
3
0
9
RF
D
N
A
S
e
q
u
e
n
c
e
0
.
2
3
0
5
0
.
9
0
6
8
0
.
2
3
0
5
0
.
2
4
1
4
0
.
9
9
2
4
X
G
B
o
o
st
D
N
A
S
e
q
u
e
n
c
e
0
.
2
6
9
1
0
.
9
0
7
4
0
.
2
6
9
1
0
.
2
9
9
6
0
.
9
9
8
0
M
LP
D
N
A
S
e
q
u
e
n
c
e
0
.
5
7
5
8
0
.
9
1
5
2
0
.
5
7
5
8
0
.
6
5
1
2
0
.
9
9
9
8
O
u
r
s
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
0
.
9
9
9
1
1
.
0
0
0
0
0
.
9
9
1
7
0
.
9
9
5
9
1
.
0
0
0
0
3
.
2
.
2
.
P
er
f
o
r
m
a
nce
co
m
pa
riso
n f
o
r
a
ll c
la
s
s
if
iers us
ing
hy
dro
ph
o
bicity
s
ig
na
l a
s
inp
ut
f
ea
t
ures
I
n
th
is
p
h
ase,
we
co
m
p
a
r
e
th
e
p
er
f
o
r
m
an
ce
o
f
v
ar
io
u
s
class
if
ier
s
u
s
in
g
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als
as
in
p
u
t
f
ea
tu
r
es.
Hy
d
r
o
p
h
o
b
icit
y
s
ig
n
als
ar
e
in
p
u
t
f
o
r
t
h
e
co
n
v
o
lu
tio
n
a
l
n
eu
r
al
n
etwo
r
k
an
d
b
aselin
e
m
ac
h
i
n
e
lear
n
in
g
m
o
d
els.
T
h
e
co
m
p
ar
is
o
n
f
o
cu
s
es
o
n
k
ey
m
etr
ics
s
u
ch
as
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
e
ca
ll,
F1
-
s
co
r
e,
an
d
AUC
-
R
O
C
to
ev
alu
ate
th
e
ef
f
ec
tiv
en
ess
o
f
ea
ch
class
if
ier
.
T
h
e
r
esu
lts
o
f
o
u
r
ex
p
e
r
im
en
t
s
ar
e
s
u
m
m
ar
ized
in
T
ab
le
5
,
p
r
o
v
i
d
in
g
a
c
o
m
p
r
e
h
en
s
iv
e
ev
alu
atio
n
o
f
ea
ch
m
o
d
el's p
er
f
o
r
m
an
ce
.
T
ab
le
5
.
C
o
m
p
a
r
ativ
e
p
er
f
o
r
m
an
ce
o
f
class
if
ier
s
u
s
in
g
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al
o
n
e
x
ten
d
ed
test
s
ets
C
l
a
s
si
f
i
e
r
F
e
a
t
u
r
e
Ty
p
e
A
c
c
.
P
r
e
c
.
R
e
c
.
F1
AUC
-
R
O
C
S
V
M
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
1
.
0
0
0
0
1
.
0
0
0
0
1
.
0
0
0
0
1
.
0
0
0
0
1
.
0
0
0
0
K
N
N
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
0
.
9
6
6
7
0
.
7
6
1
0
1
.
0
0
0
0
0
.
8
6
4
3
0
.
9
9
8
0
LR
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
0
.
9
9
3
0
0
.
9
4
4
9
0
.
9
9
1
7
0
.
9
6
7
7
0
.
9
9
7
0
DT
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
0
.
8
5
1
0
0
.
4
1
5
8
1
.
0
0
0
0
0
.
5
8
7
4
0
.
9
1
6
7
RF
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
0
.
9
9
7
4
0
.
9
8
3
6
0
.
9
9
1
7
0
.
9
8
7
7
0
.
9
9
9
6
X
G
B
o
o
st
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
0
.
9
9
1
2
0
.
9
3
0
2
0
.
9
9
1
7
0
.
9
6
0
0
0
.
9
9
7
0
M
LP
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
0
.
9
9
7
4
0
.
9
8
3
6
0
.
9
9
1
7
0
.
9
8
7
7
0
.
9
9
7
5
O
u
r
s
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
1
.
0
0
0
0
1
.
0
0
0
0
1
.
0
0
0
0
1
.
0
0
0
0
1
.
0
0
0
0
T
h
e
b
aselin
e
m
eth
o
d
s
ex
h
ib
i
ted
a
r
an
g
e
o
f
p
er
f
o
r
m
a
n
ce
o
u
tco
m
es
wh
en
u
s
in
g
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als
as
in
p
u
t
f
ea
tu
r
es.
T
h
e
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
S
VM
)
ac
h
iev
ed
p
er
f
ec
t
s
co
r
es
ac
r
o
s
s
all
m
etr
ics,
d
em
o
n
s
tr
atin
g
its
ex
ce
p
tio
n
al
class
if
icatio
n
ca
p
ab
ilit
y
wit
h
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als.
Ou
r
p
r
o
p
o
s
ed
m
eth
o
d
m
atch
ed
th
is
p
er
f
o
r
m
a
n
ce
,
also
ac
h
iev
in
g
p
er
f
ec
t
s
co
r
es,
u
n
d
er
s
co
r
in
g
its
r
o
b
u
s
tn
ess
an
d
ef
f
ec
tiv
en
ess
.
T
h
e
K
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
m
o
d
el
s
h
o
wed
an
im
p
r
ess
iv
e
r
ec
all
o
f
1
0
0
.
0
0
%,
h
ig
h
lig
h
tin
g
its
ab
ilit
y
to
id
en
ti
f
y
all
tr
u
e
p
o
s
itiv
e
ca
s
es.
Ho
wev
er
,
its
lo
wer
p
r
ec
is
i
o
n
(
7
6
.
1
0
%)
an
d
F1
-
s
co
r
e
(
8
6
.
4
3
%)
in
d
icate
a
h
ig
h
er
in
cid
en
ce
o
f
f
alse
p
o
s
itiv
es,
wh
ich
im
p
ac
ts
its
o
v
er
a
ll
r
eliab
ilit
y
.
L
o
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
d
is
p
lay
ed
r
o
b
u
s
t
p
er
f
o
r
m
an
ce
with
a
9
9
.
3
0
%
ac
cu
r
ac
y
an
d
a
9
6
.
7
7
%
F1
-
s
co
r
e,
in
d
icatin
g
a
well
-
b
alan
ce
d
m
o
d
el
th
at
ef
f
ec
tiv
ely
m
an
a
g
es
p
r
ec
is
io
n
an
d
r
ec
all.
T
h
is
r
esu
lt
m
a
k
es
L
R
a
r
eliab
le
ch
o
ice
f
o
r
class
if
icatio
n
task
s
.
r
an
d
o
m
f
o
r
est
(
R
F)
an
d
XG
B
o
o
s
t
also
p
er
f
o
r
m
ed
ad
m
ir
ab
ly
,
with
ac
cu
r
ac
ies
o
f
9
9
.
7
4
%
an
d
9
9
.
1
2
%,
r
esp
ec
tiv
ely
,
an
d
h
ig
h
F1
s
co
r
es.
W
h
ile
s
lig
h
tly
tr
ailin
g
o
u
r
p
r
o
p
o
s
ed
m
et
h
o
d
,
th
eir
s
tr
o
n
g
p
er
f
o
r
m
an
ce
in
d
icate
s
th
eir
s
u
itab
ilit
y
f
o
r
h
a
n
d
lin
g
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als.
Dec
is
io
n
t
r
ee
(
DT
)
f
ac
ed
s
ig
n
if
ican
t
ch
allen
g
es,
ev
i
d
en
ce
d
b
y
its
l
o
wer
ac
cu
r
ac
y
o
f
8
5
.
1
0
%
a
n
d
s
u
b
s
tan
tial
d
r
o
p
s
in
p
r
ec
is
io
n
(
4
1
.
5
8
%)
an
d
F1
-
s
co
r
e
(
5
8
.
7
4
%).
T
h
ese
r
esu
lt
s
h
ig
h
lig
h
t
th
e
d
if
f
ic
u
lty
D
T
m
o
d
els
f
ac
e
in
m
an
ag
in
g
th
e
c
o
m
p
lex
it
y
o
f
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al
d
ata.
T
h
e
m
u
ltil
ay
er
p
e
r
ce
p
tr
o
n
(
ML
P)
p
er
f
o
r
m
ed
s
ig
n
if
ican
tly
b
et
ter
th
an
p
r
e
v
io
u
s
ly
n
o
ted
,
with
an
ac
cu
r
ac
y
o
f
9
9
.
7
4
%,
p
r
ec
is
io
n
o
f
9
8
.
3
6
%,
r
e
ca
ll
o
f
9
9
.
1
7
%,
a
n
d
an
F1
-
s
co
r
e
o
f
9
8
.
7
7
%.
T
h
is
s
u
b
s
tan
tial
im
p
r
o
v
em
en
t
p
o
s
itio
n
s
ML
P
as
a
s
tr
o
n
g
co
n
te
n
d
er
am
o
n
g
th
e
class
if
ier
s
,
th
o
u
g
h
s
till
s
lig
h
tly
b
eh
in
d
t
h
e
p
er
f
ec
t sco
r
es a
ch
i
ev
ed
b
y
SVM
an
d
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
.
Ov
er
all,
th
e
v
ar
iab
ilit
y
in
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
b
aselin
e
m
eth
o
d
s
h
ig
h
lig
h
ts
ea
ch
m
o
d
el's
u
n
iq
u
e
s
tr
en
g
th
s
an
d
wea
k
n
ess
es.
T
h
e
p
er
f
ec
t
s
co
r
es
ac
h
iev
ed
b
y
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
an
d
S
VM
em
p
h
asize
th
e
r
o
b
u
s
tn
ess
an
d
r
eliab
ilit
y
o
f
t
h
ese
ap
p
r
o
ac
h
es
in
u
s
in
g
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als
f
o
r
v
ir
al
class
if
icatio
n
.
T
h
e
f
in
d
in
g
s
u
n
d
er
s
co
r
e
th
e
im
p
o
r
tan
ce
o
f
s
elec
tin
g
th
e
ap
p
r
o
p
r
iate
class
if
ier
f
o
r
s
p
ec
if
i
c
task
s
,
with
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
o
f
f
er
in
g
a
w
ell
-
b
alan
ce
d
an
d
ef
f
icien
t so
lu
tio
n
f
o
r
h
a
n
d
lin
g
ex
te
n
d
ed
test
s
ets.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
2
,
Feb
r
u
a
r
y
20
25
:
1
2
9
4
-
1
3
0
5
1302
3
.
3
.
Co
m
pu
t
a
t
io
n t
i
m
e
a
s
s
ess
m
ent
T
h
is
s
u
b
s
ec
tio
n
ev
alu
ates
th
e
co
m
p
u
tatio
n
tim
e
r
e
q
u
ir
ed
to
tr
ain
th
e
m
o
d
els,
p
r
o
ce
s
s
r
aw
s
eq
u
en
ce
s
in
to
im
ag
es,
an
d
m
ak
e
p
r
e
d
ictio
n
s
o
n
n
ew
d
ata.
T
h
e
a
n
aly
s
is
aim
s
to
ass
ess
th
e
p
r
ac
ticality
an
d
ef
f
icien
cy
o
f
o
u
r
p
r
o
p
o
s
ed
m
et
h
o
d
in
r
ea
l
-
wo
r
ld
ap
p
licatio
n
s
.
T
o
g
au
g
e
co
m
p
u
tatio
n
al
ef
f
icien
cy
,
we
m
ea
s
u
r
ed
th
e
tim
e
n
ee
d
ed
f
o
r
v
ar
io
u
s
p
r
o
ce
s
s
s
tag
es,
in
clu
d
in
g
m
o
d
el
t
r
ain
in
g
,
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
an
d
p
r
e
d
ictio
n
.
W
e
r
ec
o
r
d
ed
th
e
tim
e
r
eq
u
ir
e
d
to
tr
ain
th
e
C
NN
m
o
d
el
u
s
i
n
g
a
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al
an
d
co
m
p
ar
e
d
it
to
th
e
tim
e
n
ee
d
ed
f
o
r
tr
ai
n
in
g
b
aselin
e
m
o
d
els
u
s
in
g
r
aw
DNA
s
eq
u
en
ce
s
.
Ou
r
f
in
d
in
g
s
in
d
icate
d
th
at
th
e
C
NN
m
o
d
el
d
em
a
n
d
ed
m
o
r
e
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
a
n
d
lo
n
g
e
r
tr
ain
i
n
g
tim
es
d
u
e
to
th
e
c
o
m
p
lex
ity
o
f
p
r
o
ce
s
s
in
g
im
ag
e
d
ata.
Ho
wev
er
,
th
e
in
cr
ea
s
ed
tr
ain
in
g
tim
e
is
ju
s
tifie
d
b
y
th
e
s
ig
n
if
ican
tly
h
ig
h
e
r
p
er
f
o
r
m
an
ce
m
etr
ics
th
e
C
N
N
m
o
d
el
ac
h
iev
es.
W
e
also
m
ea
s
u
r
ed
th
e
p
r
ep
r
o
ce
s
s
in
g
tim
e
n
ec
ess
ar
y
to
co
n
v
er
t
r
aw
DNA
s
eq
u
en
ce
s
in
to
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als.
T
h
is
p
r
o
ce
s
s
in
v
o
lv
es
tr
an
s
latin
g
DNA
in
to
am
in
o
ac
id
s
eq
u
en
ce
s
,
ca
lcu
latin
g
h
y
d
r
o
p
h
o
b
icity
p
r
o
f
iles
,
an
d
g
en
er
atin
g
th
e
co
r
r
esp
o
n
d
i
n
g
im
ag
es.
Alth
o
u
g
h
co
m
p
u
tatio
n
ally
in
ten
s
iv
e,
t
h
is
p
r
ep
r
o
ce
s
s
in
g
s
tep
p
r
o
v
id
es
a
r
o
b
u
s
t
f
ea
tu
r
e
r
ep
r
ese
n
tatio
n
th
at
s
ig
n
if
ican
tly
en
h
an
ce
s
th
e
m
o
d
el'
s
o
v
er
all
p
er
f
o
r
m
a
n
ce
.
Fin
ally
,
we
ass
ess
ed
th
e
tim
e
r
eq
u
ir
ed
t
o
m
a
k
e
p
r
ed
ictio
n
s
o
n
n
ew
d
ata.
T
h
e
C
NN
m
o
d
el
d
em
o
n
s
tr
ated
ef
f
icien
t p
r
ed
ictio
n
tim
e
s
co
m
p
ar
ab
le
to
th
e
b
aselin
e
m
o
d
els.
Desp
ite
th
e
in
itial
p
r
ep
r
o
ce
s
s
in
g
o
v
er
h
ea
d
,
th
e
C
NN
m
o
d
el'
s
p
r
ed
icti
o
n
p
h
ase
was
o
p
tim
ized
t
o
h
an
d
le
im
ag
e
d
ata
s
wif
tly
,
en
s
u
r
in
g
p
r
ac
tical
ap
p
licab
ilit
y
in
r
ea
l
-
tim
e
s
ce
n
ar
io
s
.
T
ab
le
6
s
u
m
m
ar
izes
th
e
co
m
p
u
tatio
n
al
ef
f
icien
cy
m
etr
ics f
o
r
b
o
th
th
e
C
NN
an
d
b
aselin
e
m
o
d
els:
T
ab
le
6
.
C
o
m
p
u
tatio
n
al
ef
f
icie
n
cy
co
m
p
ar
is
o
n
: tr
ain
in
g
an
d
p
r
ed
ictio
n
tim
es
C
l
a
s
si
f
i
e
r
F
e
a
t
u
r
e
t
y
p
e
Tr
a
i
n
i
n
g
t
i
me
(
sec
)
P
r
e
d
i
c
t
i
o
n
t
i
m
e
(
se
c
/
s
a
m
p
l
e
)
S
V
M
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
7
1
8
.
0
0
.
3
0
K
N
N
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
0
.
3
0
.
1
0
LR
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
3
3
.
5
0
.
2
0
DT
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
7
4
.
0
0
.
5
0
RF
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
2
3
.
2
0
.
1
0
X
G
B
o
o
st
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
6
7
4
.
0
0
.
1
5
M
LP
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
3
9
.
1
0
.
1
2
O
u
r
s
h
y
d
r
o
p
h
o
b
i
c
i
t
y
si
g
n
a
l
5
8
.
6
0
.
3
0
T
h
e
r
esu
lts
in
T
ab
le
6
i
n
d
icat
e
th
at
wh
ile
o
u
r
p
r
o
p
o
s
ed
C
NN
m
o
d
el
r
e
q
u
ir
ed
a
m
o
d
er
a
te
tr
ain
in
g
tim
e
(
5
8
.
6
s
ec
o
n
d
s
)
co
m
p
ar
e
d
to
s
o
m
e
b
aselin
e
m
o
d
els,
it o
f
f
er
ed
ef
f
icien
t p
r
e
d
ictio
n
tim
e
s
(
0
.
3
0
s
ec
o
n
d
s
p
er
s
am
p
le)
co
m
p
ar
ab
le
to
th
o
s
e
o
f
th
e
o
th
er
m
eth
o
d
s
.
S
p
ec
if
ically
,
SVM
an
d
XG
B
o
o
s
t
r
e
q
u
ir
ed
s
ig
n
if
ican
tly
lo
n
g
er
tr
ain
in
g
tim
es
(
7
1
8
.
0
s
ec
o
n
d
s
an
d
6
7
4
.
0
s
ec
o
n
d
s
,
r
esp
ec
tiv
ely
)
b
u
t
d
em
o
n
s
tr
ated
co
m
p
ar
a
b
le
p
r
ed
ictio
n
tim
es
(
0
.
3
0
an
d
0
.
1
5
s
ec
o
n
d
s
p
er
s
am
p
le)
.
KNN,
R
F,
an
d
ML
P
h
a
d
n
o
tab
ly
s
h
o
r
ter
tr
ain
in
g
tim
es
(
0
.
3
,
2
3
.
2
,
an
d
3
9
.
1
s
ec
o
n
d
s
,
r
esp
ec
tiv
ely
)
an
d
f
aster
p
r
e
d
ictio
n
tim
es
(
0
.
1
0
,
0
.
1
0
,
an
d
0
.
1
2
s
ec
o
n
d
s
p
er
s
am
p
le)
,
h
ig
h
lig
h
ti
n
g
th
eir
co
m
p
u
tatio
n
al
ef
f
icien
c
y
.
L
o
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
an
d
d
ec
is
io
n
tr
ee
(
DT
)
s
h
o
wed
in
ter
m
ed
iate
tr
ain
in
g
tim
es
(
3
3
.
5
an
d
7
4
.
0
s
ec
o
n
d
s
,
r
esp
ec
ti
v
ely
)
an
d
v
ar
ied
p
r
ed
ictio
n
ti
m
es
(
0
.
2
0
an
d
0
.
5
0
s
ec
o
n
d
s
p
er
s
am
p
le)
,
r
ef
lectin
g
th
eir
b
alan
ce
d
co
m
p
u
tati
o
n
al
d
em
an
d
s
.
Desp
ite
th
e
in
itial
p
r
ep
r
o
ce
s
s
in
g
o
v
er
h
ea
d
,
th
e
C
NN
m
o
d
el's
o
p
tim
ized
p
r
ed
ictio
n
p
h
ase
en
s
u
r
es
p
r
ac
tical
ap
p
licab
ilit
y
in
r
ea
l
-
tim
e
s
ce
n
ar
io
s
.
T
h
e
r
esu
lts
s
u
g
g
est
th
at
th
e
in
itial
in
v
estme
n
t
in
tr
ain
in
g
o
u
r
p
r
o
p
o
s
ed
C
NN
m
o
d
el
is
ju
s
tifie
d
b
y
its
r
o
b
u
s
t
p
er
f
o
r
m
an
ce
an
d
ef
f
icien
t
p
r
e
d
ictio
n
ca
p
ab
ilit
ies in
r
ea
l
-
wo
r
ld
ap
p
licatio
n
s
.
3
.
4
.
Co
nfusi
o
n m
a
t
rix
a
nd
AUC
-
RO
C
a
na
ly
s
is
o
f
predi
ct
io
n r
esu
lt
s
T
o
p
r
o
v
id
e
a
d
etailed
v
iew
o
f
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
'
s
p
er
f
o
r
m
an
ce
,
we
p
r
esen
t
th
e
co
n
f
u
s
io
n
m
atr
ix
f
o
r
th
e
p
r
ed
ictio
n
r
esu
lts
f
r
o
m
th
e
f
ir
s
t
ex
p
e
r
im
en
t
in
T
a
b
le
3
.
T
h
e
c
o
n
f
u
s
io
n
m
atr
ix
h
elp
s
v
is
u
alize
th
e
tr
u
e
p
o
s
itiv
es,
f
alse
p
o
s
itiv
es,
an
d
f
alse
n
eg
ativ
es,
p
r
o
v
id
in
g
in
s
ig
h
t
in
to
th
e
class
if
icati
o
n
ac
cu
r
ac
y
.
T
h
e
co
n
f
u
s
io
n
m
atr
ix
an
d
R
OC
cu
r
v
e
f
o
r
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
a
r
e
s
h
o
wn
in
Fig
u
r
e
3
.
Fro
m
th
e
c
o
n
f
u
s
io
n
m
at
r
ix
in
Fig
u
r
e
3
(
a
)
,
we
o
b
s
er
v
e
th
e
f
o
llo
win
g
:
tr
u
e
p
o
s
itiv
es
(
T
P):
6
2
2
,
tr
u
e
n
eg
ativ
es
(
T
N)
:
5
9
8
,
f
alse
p
o
s
itiv
es
(
FP
)
:
2
,
an
d
f
alse
n
eg
a
tiv
es
(
FN)
:
0
.
T
h
e
h
ig
h
n
u
m
b
er
o
f
tr
u
e
p
o
s
itiv
es
an
d
tr
u
e
n
e
g
ativ
es,
alo
n
g
wi
th
th
e
lo
w
n
u
m
b
e
r
o
f
f
alse
p
o
s
itiv
es
an
d
f
alse
n
eg
ativ
es,
in
d
icate
s
th
at
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
ac
h
iev
es
h
i
g
h
ac
cu
r
ac
y
i
n
class
if
y
in
g
th
e
test
d
ata.
T
h
ese
r
esu
lts
f
u
r
th
er
r
ein
f
o
r
ce
th
e
ef
f
ec
tiv
en
ess
an
d
r
eliab
ilit
y
o
f
o
u
r
C
NN
-
b
ased
ap
p
r
o
ac
h
u
s
in
g
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al.
I
n
ad
d
itio
n
to
th
e
co
n
f
u
s
io
n
m
atr
ix
,
we
p
r
esen
t
th
e
r
ec
eiv
er
o
p
er
atin
g
ch
a
r
ac
ter
is
tic
(
R
OC
)
cu
r
v
e
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
)
s
c
o
r
e
t
o
i
ll
u
s
t
r
at
e
t
h
e
m
o
d
e
l
'
s
p
e
r
f
o
r
m
a
n
c
e
.
T
h
e
R
OC
c
u
r
v
e
i
n
F
i
g
u
r
e
3
(
b
)
d
em
o
n
s
tr
ates
o
u
r
m
o
d
el'
s
ex
ce
llen
t
p
er
f
o
r
m
an
ce
,
with
an
AUC
-
R
O
C
s
co
r
e
o
f
9
9
.
9
9
%
.
T
h
is
n
ea
r
-
p
e
r
f
ec
t
s
co
r
e
in
d
icate
s
th
at
th
e
p
r
o
p
o
s
ed
m
eth
o
d
i
s
h
ig
h
ly
d
is
cr
im
in
ativ
e,
d
is
tin
g
u
is
h
in
g
b
et
wee
n
p
o
s
itiv
e
an
d
n
eg
ativ
e
class
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Hyd
r
o
p
h
o
b
icity
s
ig
n
a
l a
n
a
lysi
s
fo
r
r
o
b
u
s
t S
A
R
S
-
C
o
V
-
2
cla
s
s
ifica
tio
n
(
Mo
h
a
mma
d
J
a
mh
u
r
i)
1303
(
a)
(
b
)
Fig
u
r
e
3
.
Pro
p
o
s
ed
m
et
h
o
d
(
a)
c
o
n
f
u
s
io
n
m
atr
ix
a
n
d
(
b
)
R
OC
c
u
r
v
e
an
aly
s
is
f
o
r
t
h
e
C
NN
3
.
5
.
Co
m
pa
riso
n wit
h pre
v
io
us
s
t
ud
ie
s
W
e
co
m
p
ar
ed
th
is
s
tu
d
y
'
s
r
esu
lts
with
th
r
ee
p
r
io
r
s
tu
d
ies
th
at
em
p
lo
y
e
d
m
ac
h
in
e
lea
r
n
in
g
an
d
d
ee
p
lear
n
in
g
tech
n
i
q
u
es
to
class
if
y
v
ir
al
g
en
o
m
es,
s
p
ec
if
ically
o
n
SAR
S
-
C
o
V
-
2
.
R
an
d
h
awa
et
a
l
.
[
2
9
]
u
tili
ze
d
k
-
m
er
s
an
d
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
,
ac
h
iev
in
g
a
n
ac
cu
r
ac
y
o
f
1
0
0
%.
Ho
wev
er
,
th
e
lack
o
f
r
ep
o
r
ted
h
y
p
er
p
ar
am
eter
s
an
d
th
e
s
m
al
l
s
am
p
le
s
ize
in
th
eir
wo
r
k
m
ay
h
av
e
led
to
o
v
er
f
itti
n
g
,
th
u
s
co
m
p
r
o
m
is
in
g
th
e
r
ep
r
o
d
u
cib
ilit
y
an
d
g
en
er
aliza
b
ilit
y
o
f
th
eir
r
esu
lts
.
Ad
d
itio
n
ally
,
th
ey
r
ep
o
r
ted
a
c
o
m
p
u
t
atio
n
tim
e
o
f
2
.
1
4
s
ec
o
n
d
s
p
er
class
if
icatio
n
,
s
ig
n
if
ican
tly
h
i
g
h
er
th
an
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
.
L
o
p
ez
-
R
in
co
n
et
a
l
.
[
1
8
]
em
p
lo
y
ed
d
ee
p
n
e
u
r
al
n
etwo
r
k
s
(
DNNs)
,
ac
h
iev
in
g
an
ac
c
u
r
ac
y
o
f
9
8
.
7
3
%.
Desp
ite
th
e
h
ig
h
ac
cu
r
a
cy
,
th
eir
s
tu
d
y
was
lim
ited
b
y
an
im
b
ala
n
ce
d
d
ataset
an
d
th
e
ex
clu
s
io
n
o
f
a
co
n
tr
o
l
g
r
o
u
p
,
wh
ich
m
ay
af
f
ec
t
th
e
m
o
d
el'
s
g
en
er
aliza
b
ilit
y
.
Fu
r
th
er
m
o
r
e
,
th
e
DNN
a
p
p
r
o
ac
h
is
co
m
p
u
tatio
n
ally
i
n
ten
s
iv
e,
r
eq
u
ir
in
g
s
u
b
s
tan
tial
d
ata
an
d
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
.
Sin
g
h
et
a
l.
[
2
2
]
Ad
o
p
tin
g
a
d
ig
it
al
s
ig
n
al
p
r
o
ce
s
s
in
g
(
DSP)
an
d
m
ac
h
in
e
lear
n
in
g
(
ML
)
ap
p
r
o
ac
h
,
th
ey
ac
h
iev
e
d
a
s
en
s
itiv
ity
o
f
9
6
.
2
9
%,
s
p
ec
i
f
icity
o
f
9
8
.
2
5
%,
an
d
ac
c
u
r
ac
y
o
f
9
7
.
4
7
%.
T
h
ei
r
m
eth
o
d
was
n
o
tab
ly
ef
f
icien
t,
with
a
co
m
p
u
tatio
n
tim
e
o
f
0
.
3
1
s
ec
o
n
d
s
p
e
r
class
if
ic
ati
o
n
.
Ho
wev
e
r
,
th
ei
r
s
tu
d
y
'
s
r
elian
ce
o
n
a
s
m
all
n
u
m
b
er
o
f
s
am
p
les
an
d
p
ar
tial
c
o
d
in
g
s
eq
u
en
ce
s
(
C
DS)
m
ay
a
f
f
ec
t
th
e
r
o
b
u
s
tn
ess
o
f
th
eir
r
esu
lts
.
Ou
r
p
r
o
p
o
s
ed
C
NN
-
b
ased
m
e
th
o
d
u
s
in
g
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al
d
em
o
n
s
tr
ated
s
u
p
er
i
o
r
p
er
f
o
r
m
an
ce
,
ac
h
iev
in
g
o
v
e
r
9
9
.
9
1
%
ac
cu
r
a
cy
,
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e
,
an
d
AUC
-
R
OC
.
T
h
e
m
eth
o
d
s
h
o
wed
r
o
b
u
s
tn
ess
in
h
an
d
lin
g
lo
n
g
er
test
s
eq
u
en
ce
s
,
m
ain
tain
in
g
h
ig
h
p
er
f
o
r
m
an
ce
ac
r
o
s
s
all
m
etr
ics.
Ad
d
itio
n
ally
,
t
h
e
co
m
p
u
tatio
n
tim
e
f
o
r
o
u
r
m
e
th
o
d
was
o
n
ly
0
.
3
s
ec
o
n
d
s
p
er
class
if
ica
tio
n
,
m
ak
in
g
it
s
u
itab
le
f
o
r
r
ea
l
-
tim
e
ap
p
licatio
n
s
.
Ou
r
ap
p
r
o
ac
h
also
en
s
u
r
ed
b
alan
ce
d
p
e
r
f
o
r
m
an
ce
ac
r
o
s
s
all
m
etr
ics,
in
d
icatin
g
a
well
-
g
en
er
alize
d
m
o
d
el
ca
p
ab
le
o
f
f
lex
ib
le
in
p
u
t
h
an
d
lin
g
d
u
e
to
t
h
e
u
s
e
o
f
a
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al.
A
co
m
p
ar
ativ
e
s
u
m
m
ar
y
is
p
r
o
v
id
ed
i
n
T
ab
le
7
.
Desp
ite
th
e
p
r
o
m
is
in
g
r
esu
lts
,
o
u
r
s
tu
d
y
h
as
s
o
m
e
lim
itatio
n
s
.
T
h
e
co
m
p
u
tatio
n
al
co
s
t
o
f
g
en
er
atin
g
a
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al
an
d
t
r
ain
in
g
th
e
C
NN
m
o
d
el
is
h
ig
h
,
wh
ich
m
ay
lim
it its
s
ca
lab
ilit
y
.
Ad
d
itio
n
ally
,
t
h
e
m
o
d
el'
s
p
er
f
o
r
m
a
n
ce
was
n
o
t v
alid
ated
with
b
io
lo
g
ical
in
s
ig
h
ts
o
r
e
x
p
er
im
e
n
tal
d
ata,
wh
ic
h
wo
u
ld
s
tr
en
g
th
e
n
its
ap
p
licab
ilit
y
in
r
ea
l
-
wo
r
ld
v
ir
u
s
class
if
icatio
n
s
ce
n
ar
io
s
.
T
ab
le
7
.
C
o
m
p
a
r
ativ
e
a
n
aly
s
is
o
f
SAR
S
-
C
o
V
-
2
class
if
icatio
n
m
eth
o
d
s
S
t
u
d
y
M
e
t
h
o
d
A
c
c
.
P
r
e
c
.
R
e
c
.
F1
C
T
(
se
c
)
R
a
n
d
h
a
w
a
e
t
a
l
.
k
-
mers
+
M
L
1
0
0
.
0
0
%
-
-
-
2
.
1
4
Lo
p
e
z
-
R
i
n
c
o
n
e
t
a
l
.
DNN
9
8
.
7
3
%
-
-
-
-
S
i
n
g
h
e
t
a
l
.
D
S
P
+
M
L
(
R
F
)
9
7
.
4
7
%
9
6
.
2
9
%
9
8
.
2
5
%
-
0
.
3
1
O
u
r
M
e
t
h
o
d
C
N
N
+
H
y
d
r
o
p
h
o
b
i
c
i
t
y
I
mg
9
9
.
9
1
%
1
0
0
.
0
0
%
9
9
.
1
7
%
9
9
.
5
9
%
0
.
3
4.
CO
NCLU
SI
O
N
I
n
th
is
wo
r
k
,
we
h
av
e
d
em
o
n
s
tr
ated
th
e
r
o
b
u
s
tn
ess
an
d
ef
f
ec
tiv
en
ess
o
f
o
u
r
p
r
o
p
o
s
ed
class
if
icatio
n
m
o
d
el,
wh
ich
u
tili
ze
s
a
h
y
d
r
o
p
h
o
b
icity
s
ig
n
al
d
er
iv
e
d
f
r
o
m
DNA
s
eq
u
en
ce
s
to
d
is
tin
g
u
is
h
b
etwe
en
SAR
S
-
C
o
V
-
2
an
d
n
o
n
-
SAR
S
-
C
o
V
-
2
v
ir
u
s
es.
T
h
is
class
if
icatio
n
is
cr
u
cial
f
o
r
u
n
d
er
s
tan
d
in
g
v
ir
al
g
en
o
m
es
an
d
h
as
s
ig
n
if
ican
t
im
p
licatio
n
s
f
o
r
d
r
u
g
d
is
co
v
er
y
an
d
p
u
b
lic
h
ea
lt
h
.
Ou
r
ex
p
er
im
e
n
tal
r
esu
lts
s
h
o
w
th
at
o
u
r
m
o
d
el
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
s
b
as
elin
e
m
eth
o
d
s
th
at
u
s
e
r
aw
s
eq
u
en
ce
d
ata,
p
a
r
ticu
lar
ly
wh
en
h
an
d
lin
g
te
s
t
s
eq
u
en
ce
s
th
at
d
if
f
er
in
len
g
th
f
r
o
m
t
h
o
s
e
in
th
e
tr
ain
in
g
s
et.
Ou
r
p
r
o
p
o
s
ed
m
eth
o
d
a
ch
iev
ed
o
v
er
9
9
%
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e,
an
d
AUC
-
R
OC
.
T
h
is
p
er
f
o
r
m
an
ce
is
s
ig
n
if
ican
tl
y
h
ig
h
er
th
an
th
e
b
aselin
e
m
eth
o
d
s
,
wh
ich
s
tr
u
g
g
led
with
lo
n
g
er
s
eq
u
en
ce
s
an
d
attain
ed
m
u
c
h
lo
wer
av
e
r
ag
e
m
etr
ics.
T
h
ese
r
esu
lts
h
ig
h
lig
h
t
th
e
p
o
ten
tial o
f
h
y
d
r
o
p
h
o
b
icity
s
ig
n
als
as
a
s
u
p
er
io
r
f
ea
tu
r
e
r
ep
r
esen
tatio
n
,
p
r
o
v
id
in
g
a
m
o
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.