I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
11
,
No
.
2
,
A
p
r
il
2
0
2
1
,
p
p
.
1
5
6
1
~
1
5
6
9
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v
1
1
i
2
.
pp
1
5
6
1
-
1
5
6
9
1561
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
An ICA
-
ense
m
bl
e
learning
appro
a
c
hes for p
redict
io
n of
RN
A
-
seq
m
a
la
ria
vec
tor g
ene express
io
n
da
ta cla
ss
ificatio
n
M
ichea
l O
l
a
o
lu Ar
o
w
o
lo
1
,
M
a
rio
n O
.
Adebiy
i
2
,
Ay
o
dele
A.
Adebiy
i
3
,
Cha
rit
y
Are
m
u
4
1,
2,
3
De
p
a
rtm
e
n
t
o
f
Co
m
p
u
ter S
c
ien
c
e
,
L
a
n
d
m
a
r
k
Un
iv
e
rsit
y
,
O
m
u
-
A
ra
n
,
K
w
a
ra
S
tate
,
Nig
e
ria
4
De
p
a
rtme
n
t
o
f
Ag
ricu
lt
u
re
,
L
a
n
d
m
a
rk
Un
iv
e
rsit
y
,
O
m
u
-
A
ra
n
,
K
wa
ra
S
tate
,
Nig
e
ria
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
4
,
2
0
20
R
ev
i
s
ed
J
u
l 1
7
,
2
0
20
A
cc
ep
ted
Sep
2
3
,
2
0
2
0
M
a
laria
p
a
ra
sites
in
tro
d
u
c
e
o
u
tstan
d
i
n
g
li
f
e
-
p
h
a
se
v
a
riatio
n
s
a
s
th
e
y
g
ro
w
a
c
ro
ss
m
u
lt
ip
le atm
o
sp
h
e
re
s o
f
th
e
m
o
sq
u
it
o
v
e
c
to
r.
T
h
e
re
a
re
tran
sc
rip
to
m
e
s
o
f
se
v
e
r
a
l
th
o
u
sa
n
d
d
if
f
e
r
e
n
t
p
a
ra
sites
.
Rib
o
n
u
c
leic
a
c
id
se
q
u
e
n
c
i
n
g
(
RNA
-
se
q
)
is
a
p
re
v
a
len
t
g
e
n
e
e
x
p
re
ss
io
n
t
o
o
l
lea
d
i
n
g
to
b
e
tt
e
r
u
n
d
e
rsta
n
d
i
n
g
o
f
g
e
n
e
ti
c
in
terro
g
a
ti
o
n
s.
RNA
-
s
e
q
m
e
a
su
re
s
tran
sc
rip
ti
o
n
s
o
f
e
x
p
re
ss
io
n
s
o
f
g
e
n
e
s.
Da
ta
f
ro
m
RN
A
-
s
e
q
n
e
c
e
ss
it
a
te
p
ro
c
e
d
u
ra
l
e
n
h
a
n
c
e
m
e
n
ts
in
m
a
c
h
in
e
lea
rn
in
g
tec
h
n
iq
u
e
s.
Re
se
a
rc
h
e
rs
h
a
v
e
su
g
g
e
ste
d
v
a
rio
u
s a
p
p
ro
a
c
h
e
d
lea
rn
in
g
f
o
r
th
e
stu
d
y
o
f
b
io
lo
g
ica
l
d
a
ta.
T
h
is
stu
d
y
w
o
rk
s
o
n
IC
A
fe
a
tu
re
e
x
tra
c
ti
o
n
a
lg
o
rit
h
m
to
re
a
li
z
e
d
o
rm
a
n
t
c
o
m
p
o
n
e
n
ts
f
r
o
m
a
h
u
g
e
d
i
m
e
n
s
i
o
n
a
l
R
N
A
-
s
e
q
v
e
c
t
o
r
d
a
t
a
s
e
t
,
a
n
d
e
s
t
i
m
a
t
e
s
i
t
s
c
l
a
s
s
i
f
i
c
a
t
i
o
n
p
e
r
f
o
r
m
a
n
c
e
,
E
n
s
e
m
b
l
e
c
l
a
s
s
i
f
i
c
a
t
i
o
n
a
l
g
o
r
i
t
h
m
i
s
u
s
e
d
i
n
c
a
rr
y
in
g
o
u
t
th
e
e
x
p
e
rim
e
n
t.
T
h
i
s
stu
d
y
is
tes
ted
o
n
RNA
-
se
q
m
o
sq
u
it
o
a
n
o
p
h
e
les
g
a
m
b
iae
d
a
tas
e
t.
T
h
e
re
s
u
lt
s
o
f
th
e
e
x
p
e
ri
m
e
n
t
o
b
tain
e
d
a
n
o
u
t
p
u
t
m
e
tri
c
s w
it
h
a
9
3
.
3
%
c
las
sif
ica
ti
o
n
a
c
c
u
ra
c
y
.
K
ey
w
o
r
d
s
:
E
n
s
e
m
b
le
class
if
ier
I
C
A
Ma
lar
ia
v
ec
to
r
RN
A
-
s
eq
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
:
Mic
h
ea
l O
lao
l
u
A
r
o
w
o
lo
Dep
ar
t
m
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
L
a
n
d
m
ar
k
U
n
i
v
er
s
it
y
O
m
u
-
A
r
a
n
,
K
w
ar
a
State,
Ni
g
e
r
ia
E
m
ail:
ar
o
w
o
lo
.
m
ic
h
ea
l@
l
m
u
.
ed
u
.
n
g
1.
I
NT
RO
D
UCT
I
O
N
Nex
t
-
g
e
n
er
atio
n
s
eq
u
e
n
ci
n
g
t
ec
h
n
o
lo
g
y
h
as
cr
ea
ted
s
ev
er
al
w
id
e
d
atase
ts
,
t
h
at
allo
w
s
b
i
o
lo
g
is
ts
to
ex
a
m
in
e
an
d
d
eter
m
in
e
d
if
f
ic
u
lt
g
e
n
e
tr
an
s
cr
ip
ts
s
u
c
h
a
s
R
N
A
r
elatio
n
s
h
ip
s
a
n
d
ail
m
e
n
ts
s
u
ch
as
ca
n
ce
r
,
co
n
tag
io
n
s
(
m
alar
ia)
,
tu
m
o
r
s
,
h
er
ed
ities
,
b
io
lo
g
ical,
a
m
o
n
g
o
t
h
er
s
[
1
]
.
I
n
Af
r
ica,
m
o
s
q
u
ito
an
o
p
h
ele
s
g
a
m
b
iae
ar
e
b
lo
o
d
-
s
u
c
k
i
n
g
p
ar
asit
es
w
it
h
lar
g
e
p
at
h
w
a
y
s
to
P
las
m
o
d
iu
m
F
alcip
ar
u
m
.
A
n
o
p
h
eles
m
o
s
q
u
ito
e
s
is
a
d
ea
d
ly
m
alar
ia
p
ar
asit
e,
ac
co
u
n
tab
le
f
o
r
t
h
o
u
s
a
n
d
s
o
f
d
ea
th
s
.
A
s
b
attle
w
it
h
an
t
i
m
alar
ia
s
u
p
p
o
s
ito
r
ies
b
an
q
u
ets
u
p
s
u
r
g
es,
p
er
ce
p
tiv
e
s
f
o
r
s
tate
-
of
-
t
h
e
-
ar
t
d
r
u
g
s
n
ec
ess
itate
s
i
m
p
r
o
v
ed
b
io
lo
g
ical
k
n
o
w
led
g
e
o
f
th
e
s
e
k
in
d
.
Mo
s
q
u
i
to
an
o
p
h
eles
o
r
g
an
i
s
m
ap
p
r
o
v
ed
p
r
ec
is
e
g
en
e
ex
p
r
ess
io
n
co
n
tr
o
ls
h
as
b
ee
n
a
m
aj
o
r
co
n
ce
r
n
n
ee
d
in
g
an
i
m
p
r
o
v
ed
q
u
an
titat
iv
e
p
r
ed
ictiv
e
m
a
lar
ia
v
e
cto
r
t
r
an
s
cr
ip
ts
m
o
d
el
[
2
,
3
].
R
N
A
-
s
eq
lear
n
in
g
p
r
o
d
u
ce
s
s
en
s
iti
v
e
b
io
lo
g
ica
l
p
er
ce
p
tiv
e
i
n
v
esti
g
atio
n
s
b
y
r
ec
o
g
n
iz
in
g
a
p
r
eli
m
in
ar
y
b
io
lo
g
ical
en
h
a
n
c
ed
s
eq
u
en
ci
n
g
p
u
r
p
o
s
e
f
u
l
p
lan
an
al
y
s
is
.
R
N
A
-
s
eq
d
ata
in
cl
u
d
es
th
e
r
e
m
o
v
al
o
f
th
e
h
ig
h
-
d
i
m
e
n
s
io
n
alit
y
c
u
r
s
es
in
a
d
ata,
s
u
c
h
as
:
d
is
tu
r
b
an
ce
s
,
r
ep
etitio
n
s
,
i
n
co
n
s
is
t
en
cies,
r
ed
u
n
d
an
c
y
,
ir
r
elev
an
t,
in
co
r
r
ec
t,
i
n
v
alid
,
a
m
o
n
g
o
t
h
er
s
[
4
]
.
R
ec
e
n
t
i
n
n
o
v
atio
n
s
h
a
v
e
e
n
h
an
ce
d
ap
p
r
o
ac
h
es
f
o
r
d
esi
g
n
in
g
s
tate
-
of
-
th
e
-
ar
t
h
ea
l
th
ca
r
e
m
o
d
els
s
u
c
h
as
ad
ap
ted
th
er
ap
ie
s
,
in
telli
g
e
n
t
h
ea
lt
h
s
u
r
v
eilla
n
ce
s
y
s
te
m
s
,
a
m
o
n
g
o
th
er
d
is
ea
s
e
d
iag
n
o
s
es
[
5
].
N
u
m
e
r
o
u
s
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
m
e
t
h
o
d
s
w
i
t
h
p
r
a
c
t
i
c
a
l
a
d
v
a
n
c
e
s
h
a
v
e
p
e
e
n
d
e
v
e
l
o
p
e
d
t
h
r
o
u
g
h
t
h
e
y
ea
r
s
to
an
al
y
ze
th
e
e
n
o
r
m
o
u
s
v
o
lu
m
e
o
f
R
N
A
-
s
eq
an
d
d
ata
ex
p
r
ess
io
n
o
f
n
e
x
t
g
e
n
er
atio
n
g
en
e
s
eq
u
e
n
ci
n
g
b
y
s
tu
d
y
in
g
t
h
e
r
elate
d
b
io
lo
g
ic
all
y
o
u
tli
n
es
[
6
]
.
R
esear
c
h
er
s
h
a
v
e
u
s
ed
m
ac
h
in
e
lear
n
i
n
g
tec
h
n
iq
u
es
w
it
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
2
,
A
p
r
il 2
0
2
1
:
1
56
1
-
1569
1562
v
ar
iab
le
p
er
f
o
r
m
a
n
ce
le
v
els
f
o
r
R
N
A
-
s
eq
g
e
n
e
ex
p
r
es
s
io
n
d
ata
[
7
,
8
].
C
o
m
p
u
tatio
n
a
l
ap
p
r
o
ac
h
es
h
a
v
e
r
e
m
ain
ed
ap
p
lieca
b
le
to
lar
g
e
g
e
n
etic
ail
m
en
t
s
d
atab
ase
s
o
f
p
er
s
o
n
s
,
g
e
n
es
ca
n
b
e
f
o
u
n
d
r
esp
o
n
s
ib
le
f
o
r
th
e
p
r
esen
ce
o
f
ail
m
e
n
ts
.
Nu
m
er
o
u
s
ap
p
r
o
ac
h
es
ar
e
u
s
ed
in
d
etec
tin
g
d
i
f
f
er
e
n
tiall
y
e
x
p
r
e
s
s
ed
g
en
e
s
(
DE
G)
.
P
r
o
ce
d
u
r
es
o
f
d
ata
m
i
n
in
g
ar
e
s
ig
n
i
f
ica
n
t
i
n
id
e
n
ti
f
y
in
g
t
h
e
d
if
f
er
e
n
ce
s
b
et
w
ee
n
g
e
n
e
s
d
er
iv
ed
f
r
o
m
t
h
e
h
u
m
a
n
g
e
n
o
m
e.
Nu
m
er
o
u
s
m
ac
h
in
e
lear
n
in
g
m
et
h
o
d
s
ar
e
e
m
u
lated
a
n
d
u
s
ed
i
n
e
x
a
m
in
in
g
a
n
d
id
en
ti
f
y
i
n
g
ex
p
r
ess
io
n
o
f
v
ar
io
u
s
g
en
e
p
r
o
f
ilin
g
d
is
ea
s
e
s
.
Gen
e
ex
p
r
e
s
s
io
n
p
r
o
f
ili
n
g
an
d
its
ap
p
r
o
ac
h
es
b
y
m
ea
n
s
o
f
n
u
m
er
o
u
s
d
ata
m
i
n
i
n
g
ar
e
in
d
is
p
en
s
ab
le.
R
e
s
ea
r
ch
w
o
r
k
s
h
av
e
b
ee
n
p
r
o
p
o
s
ed
b
y
n
u
m
er
o
u
s
au
t
h
o
r
s
in
th
is
ar
ea
,
ex
is
tin
g
r
esear
ch
e
s
ar
e
k
n
o
w
n
i
n
s
tu
d
y
i
n
g
g
e
n
e
ex
p
r
es
s
io
n
s
[
5
]
.
B
lo
o
d
-
b
ased
s
ig
n
at
u
r
e
g
en
e
ex
p
r
ess
io
n
an
d
d
at
a
m
i
n
in
g
f
o
r
d
is
ea
s
es
in
id
en
tify
i
n
g
tr
a
n
s
cr
ip
ts
t
h
at
ca
n
b
e
u
s
ed
in
clas
s
i
f
icatio
n
is
p
r
o
p
o
s
ed
[
9
]
.
Usi
n
g
Gen
e
e
x
p
r
ess
io
n
o
m
n
ib
u
s
d
atab
ase
f
r
o
m
R
N
A
d
ata
an
d
u
s
in
g
m
ac
h
in
e
lear
n
i
n
g
al
g
o
r
ith
m
s
lan
g
u
ag
e
to
o
ls
,
w
o
r
k
s
o
n
R
N
A
-
s
eq
d
ata
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
b
y
d
i
m
e
n
s
io
n
alit
y
r
ed
u
ctio
n
,
cl
u
s
ter
in
g
a
n
d
class
i
f
icat
io
n
b
y
p
er
f
o
r
m
in
g
a
n
i
n
teg
r
ated
r
e
v
i
e
w
,
t
h
at
h
a
v
e
r
ec
en
tl
y
ar
o
s
e
as
p
r
ed
o
m
i
n
a
n
t
s
h
i
f
t
s
,
u
s
in
g
in
d
ir
ec
t
an
d
d
ir
ec
t
m
et
h
o
d
s
w
ith
r
ed
u
ci
n
g
s
c
-
R
N
A
-
s
eq
d
ata
d
i
m
e
n
s
io
n
ap
p
r
o
ac
h
es,
r
ep
o
r
tin
g
s
c
R
N
A
-
s
eq
d
ata
[
1
0
]
.
T
h
is
s
t
u
d
y
p
r
o
p
o
s
es a
d
i
m
en
s
i
o
n
alit
y
r
ed
u
ctio
n
m
o
d
el,
b
y
u
s
in
g
I
C
A
f
ea
t
u
r
e
ex
tr
ac
tio
n
tec
h
n
iq
u
e,
to
r
ea
lize
th
e
r
elev
a
n
t
co
r
r
elate
d
laten
t
co
m
p
o
n
e
n
t
s
i
n
a
h
i
g
h
d
i
m
en
s
io
n
al
d
ataset
in
t
h
e
g
e
n
e
e
x
p
r
ess
io
n
d
ata
an
al
y
z
s
is
,
a
S
u
b
-
s
p
ac
e
g
r
o
u
p
E
n
s
e
m
b
le
class
i
f
icat
io
n
s
y
s
te
m
is
u
s
ed
in
lear
n
i
n
g
d
is
cr
ete
b
io
lo
g
ical
o
u
tlin
e
s
th
at
h
elp
s
ac
h
ie
v
e
d
ev
elo
p
ed
class
i
f
icatio
n
ac
c
u
r
ac
y
a
n
d
s
u
g
g
e
s
ted
as
a
n
e
f
f
ec
ti
v
e
p
r
o
ce
d
u
r
e
f
o
r
t
h
e
f
in
d
i
n
g
o
f
in
n
o
v
ati
v
e
g
en
e
s
f
o
r
m
alar
i
a.
2.
P
RO
P
O
SE
D
M
E
T
H
O
D
I
n
th
i
s
s
t
u
d
y
,
a
s
u
m
m
ar
ized
p
r
o
p
o
s
ed
f
r
am
e
w
o
r
k
i
n
Fi
g
u
r
e
1
is
ad
o
p
ted
,
th
e
f
u
n
d
a
m
en
ta
l
id
ea
is
to
p
r
ed
ict
m
ac
h
i
n
e
lear
n
i
n
g
tas
k
o
n
h
i
g
h
d
i
m
e
n
s
io
n
al
R
N
A
-
s
e
q
d
ata,
f
o
r
ce
lls
an
d
g
e
n
es
i
n
t
o
lo
w
er
d
i
m
e
n
s
i
o
n
a
l
d
a
t
a
s
e
t
.
T
h
e
p
l
a
n
i
s
a
d
j
u
s
t
e
d
t
o
f
e
t
c
h
o
u
t
i
m
p
o
r
t
a
n
t
d
a
t
a
i
n
a
g
i
v
e
n
d
a
t
a
s
e
t
b
y
u
t
i
l
i
z
i
n
g
I
C
A
f
e
a
t
u
r
e
e
x
t
r
a
c
t
i
o
n
m
e
t
h
o
d
a
s
a
s
t
a
g
e
.
T
o
e
v
a
l
u
a
t
e
t
h
e
p
e
r
f
o
r
m
a
n
c
e
o
f
R
N
A
-
s
e
q
d
a
t
a
s
e
t
,
E
n
s
e
m
b
l
e
cla
s
s
i
f
ica
tio
n
al
g
o
r
ith
m
s
ar
e
co
m
p
ar
ed
.
Fig
u
r
e
1
.
P
r
o
p
o
s
ed
f
r
am
e
w
o
r
k
Nu
m
er
o
u
s
ap
p
r
o
ac
h
es
o
n
m
ac
h
in
e
lear
n
i
n
g
h
av
e
b
ee
n
em
u
lated
to
ex
a
m
in
e
a
n
d
id
en
ti
f
y
g
e
n
e
ex
p
r
ess
io
n
p
r
o
f
iles
o
f
s
e
v
er
al
ail
m
e
n
ts
.
T
h
er
e
is
d
i
s
cu
s
s
io
n
o
f
t
h
e
n
ec
es
s
it
y
f
o
r
ex
p
r
es
s
io
n
o
f
g
en
e
p
r
o
f
ili
n
g
an
d
ap
p
r
o
ac
h
es
u
s
i
n
g
s
p
ec
i
f
ic
d
ata
m
i
n
i
n
g
tech
n
iq
u
es.
N
u
m
e
r
o
u
s
i
n
v
e
s
ti
g
atio
n
s
ca
r
r
ied
o
u
t
b
y
r
esear
ch
er
s
i
n
th
is
ar
ea
ar
e
co
n
s
u
lted
,
r
ec
en
t
in
v
est
ig
at
io
n
s
in
a
n
al
y
s
i
n
g
g
en
e
e
x
p
r
ess
io
n
s
ar
e
r
e
v
ie
w
e
d
[
5
]
.
A
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
i
n
g
m
et
h
o
d
f
o
r
v
ar
iet
y
o
f
R
N
A
-
s
eq
s
e
g
m
en
t
w
a
s
p
r
o
p
o
s
ed
b
y
r
an
k
i
n
g
h
u
g
e
s
ets
o
f
s
e
g
m
en
t
s
m
ea
s
u
r
ed
w
it
h
R
N
A
-
s
eq
,
u
s
i
n
g
r
an
d
o
m
f
o
r
est
class
i
f
ier
v
ar
iab
le
r
an
k
m
ea
s
u
r
e
m
e
n
t
s
,
s
p
ec
if
y
i
n
g
th
e
E
P
S
(
ex
tr
e
m
e
p
s
e
u
d
o
-
s
a
m
p
les)
f
r
eq
u
en
c
y
,
w
i
th
v
ar
iatio
n
al
a
u
t
o
en
co
d
er
r
eg
r
ess
o
r
s
in
th
e
R
NA
-
s
eq
ex
tr
ac
tio
n
r
an
k
s
o
f
ca
n
ce
r
d
ata
s
ets
w
it
h
ab
o
u
t
1
,
2
1
0
s
a
m
p
le
s
.
R
es
u
lts
i
n
t
h
e
R
N
A
-
s
eq
tr
ai
n
i
n
g
d
e
m
o
n
s
tr
ated
a
s
u
p
er
v
i
s
ed
h
id
d
en
lear
n
in
g
-
b
ased
f
ea
t
u
r
e
s
e
lectio
n
m
e
th
o
d
an
d
h
i
g
h
li
g
h
ted
th
e
n
ee
d
f
o
r
g
en
e
a
s
s
o
r
t
m
en
t
m
et
h
o
d
s
f
o
r
g
en
e
e
x
p
r
ess
io
n
an
al
y
s
is
[
1
1
]
.
C
lass
i
f
ica
tio
n
o
f
R
N
A
-
s
eq
d
ataset
u
s
in
g
s
u
p
er
v
is
ed
m
o
d
el
w
a
s
p
r
o
p
o
s
ed
f
o
r
a
g
en
er
alize
d
m
et
h
o
d
o
f
h
ig
h
l
y
ac
c
u
r
ate
s
i
n
g
le
ce
ll
clas
s
i
f
icatio
n
s
,
b
y
in
teg
r
at
in
g
u
n
b
iased
co
llectio
n
o
f
co
n
d
e
n
s
ed
d
i
m
en
s
io
n
al
s
p
ac
e
f
ea
tu
r
e
s
elec
t
io
n
tech
n
iq
u
e.
Sc
-
P
r
ed
w
a
s
u
s
ed
o
n
R
N
A
-
s
eq
p
an
cr
ea
tic
tis
s
u
e,
co
lo
r
ec
tal
tu
m
o
u
r
ce
ll
r
e
m
o
v
al,
m
o
n
o
n
u
cl
ea
r
ce
lls
,
a
n
d
m
i
x
i
n
g
d
en
d
r
iti
c
ce
lls
d
ata
s
ets.
Sc
-
P
r
ed
d
em
o
n
s
tr
ated
a
h
i
g
h
cla
s
s
if
ied
d
is
cr
ete
ce
ll
s
ac
cu
r
ac
y
[
1
2
]
.
R
N
A
-
DN
A
m
ac
h
i
n
e
lear
n
in
g
an
al
y
s
i
s
w
a
s
p
r
o
p
o
s
ed
o
n
a
lo
w
e
x
p
r
ess
ed
g
e
n
o
m
e
t
h
at
c
o
u
ld
b
e
af
f
ec
ted
co
llectiv
el
y
b
y
P
A
H
d
is
ea
s
e.
A
s
tate
-
of
-
t
h
e
-
ar
t
f
ea
tu
r
e
s
elec
tio
n
p
r
o
ce
d
u
r
e
to
class
i
f
y
a
n
ir
r
elev
a
n
t
r
an
g
e
o
f
v
er
y
b
en
e
f
icial
g
e
n
e
s
.
S
m
all
e
x
p
r
ess
io
n
cl
u
s
ter
ed
g
en
e
s
w
er
e
d
i
s
co
v
er
ed
at
p
r
e
d
ictin
g
tr
an
s
f
o
r
m
ed
P
A
H
p
r
o
ce
d
u
r
es
[
1
3
]
.
Sto
m
ac
h
c
an
ce
r
g
en
e
e
x
p
r
ess
io
n
d
ata
class
i
f
icatio
n
w
as
d
e
v
elo
p
ed
u
s
in
g
d
ee
p
lear
n
i
n
g
ap
p
r
o
ac
h
,
Hea
t
m
ap
s
,
P
C
A
,
an
d
C
N
N
al
g
o
r
ith
m
.
R
N
A
-
s
eq
g
en
e
d
ata
ex
p
r
es
s
io
n
s
t
u
d
ied
th
e
g
en
e
s
a
n
d
an
al
y
s
ed
t
h
e
m
,
9
5
.
9
6
%
an
d
5
0
.
5
1
%
w
er
e
ac
h
ie
v
ed
[
1
4
]
.
T
r
an
s
cr
ip
tio
n
s
o
f
R
N
A
-
s
eq
m
alar
i
a
d
ata
t
h
r
o
u
g
h
d
is
s
i
m
ilar
it
y
o
f
tec
h
n
iq
u
es
t
o
d
ec
o
n
v
o
lu
te
d
is
p
ar
it
y
tr
an
s
cr
ip
tio
n
f
o
r
d
is
s
i
m
ilar
m
ala
r
ia
p
ar
asit
es
w
er
e
r
ev
ea
led
u
s
i
n
g
h
id
d
en
tr
an
s
cr
ip
tio
n
al
d
is
cr
ete
s
ig
n
at
u
r
es
[
1
5
]
.
Su
p
er
v
is
ed
d
atam
i
n
i
n
g
a
p
p
r
o
ac
h
es
s
u
ch
a
s
C
4
.
5
,
b
o
o
s
ted
an
d
b
ag
g
ed
e
n
s
e
m
b
le
cla
s
s
i
f
icat
io
n
al
g
o
r
it
h
m
f
o
r
ca
n
ce
r
d
ata
w
er
e
p
r
o
p
o
s
ed
o
n
o
p
en
l
y
av
ailab
le
o
n
co
g
e
n
ic
m
icr
o
ar
r
ay
d
ata
an
d
co
r
r
elate
d
,
th
e
b
o
o
s
t
an
d
b
ag
e
n
s
e
m
b
le
clas
s
i
f
ica
tio
n
o
u
tp
er
f
o
r
m
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
I
C
A
-
en
s
emb
le
lea
r
n
in
g
a
p
p
r
o
a
ch
es fo
r
p
r
ed
ictio
n
o
f
…
(
Mich
ea
l O
la
o
lu
A
r
o
w
o
lo
)
1563
C
4
.
5
[
1
6
]
.
A
d
iag
n
o
s
tic
cla
s
s
i
f
icatio
n
u
s
i
n
g
en
s
e
m
b
le
al
g
o
r
ith
m
m
et
h
o
d
f
o
r
g
en
o
m
ic
ca
n
c
er
d
ata
ex
p
r
ess
io
n
w
a
s
d
esig
n
ed
u
s
i
n
g
R
FE
to
f
etch
ef
f
icie
n
t
f
ea
tu
r
es
f
o
r
en
h
an
ce
d
class
i
f
icatio
n
r
esu
lt
s
u
s
in
g
A
d
a
B
o
o
s
t
[
1
7
]
.
C
las
s
i
f
icatio
n
o
f
ca
n
ce
r
g
e
n
e
d
ata
ex
p
r
ess
io
n
,
w
as
ca
r
r
ied
o
u
t
u
s
i
n
g
ef
f
ec
ti
v
e
e
n
s
e
m
b
l
e
lear
n
in
g
m
eth
o
d
u
p
s
u
r
g
i
n
g
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
class
if
icatio
n
o
f
t
h
e
o
u
tc
o
m
e
r
es
u
lt
s
,
w
it
h
a
r
ed
u
ce
d
am
o
u
n
t
o
f
d
ep
en
d
en
t
o
n
o
r
ig
i
n
alitie
s
o
f
in
d
i
v
id
u
a
l
t
r
ain
in
g
s
et
[
1
8
]
.
An
e
n
h
an
ce
d
en
s
e
m
b
le
cla
s
s
i
f
icat
io
n
lear
n
in
g
w
r
ap
p
er
-
b
ased
f
ea
t
u
r
e
s
elec
t
io
n
a
n
d
r
a
n
d
o
m
tr
ee
s
p
r
o
ce
d
u
r
e
to
i
m
p
r
o
v
e
k
n
o
w
led
g
e,
m
ak
e
s
a
s
u
b
s
et
b
y
u
s
i
n
g
b
a
g
g
ed
a
n
d
r
an
d
o
m
tr
ee
s
.
I
r
r
elev
an
t
f
ea
tu
r
es
w
er
e
r
e
m
o
v
ed
to
s
elec
t
th
e
b
est
f
ea
tu
r
es
f
o
r
class
i
f
icati
o
n
,
u
s
i
n
g
R
F,
SVM
,
an
d
NB
w
i
th
9
2
%
ac
c
u
r
ac
y
[
19
]
.
T
ex
t
clas
s
if
icatio
n
alg
o
r
ith
m
s
w
a
s
p
r
o
p
o
s
ed
u
s
in
g
v
ar
io
u
s
te
x
t
d
i
m
en
s
i
o
n
al
it
y
r
ed
u
ct
io
n
m
eth
o
d
s
[
2
0
].
3.
RE
S
E
ARCH
M
E
T
H
O
D
Data
m
i
n
i
n
g
f
o
r
h
ig
h
d
i
m
e
n
s
i
o
n
al
d
ataset
en
h
a
n
ce
m
en
ts
h
a
v
e
b
ee
n
ca
r
r
ied
o
u
t
b
y
s
ev
er
al
au
th
o
r
s
,
I
n
d
ep
en
d
en
t
co
m
p
o
n
e
n
t
a
n
al
y
s
is
(
I
C
A
)
a
n
d
clas
s
if
icatio
n
u
s
in
g
al
g
o
r
ith
m
i
s
p
r
o
p
o
s
ed
f
o
r
R
N
A
-
s
eq
m
a
lar
ia
v
ec
to
r
d
ata.
3
.
1
.
M
a
t
er
ia
l
A
w
es
ter
n
Ken
y
a
m
o
s
q
u
ito
g
en
e
d
ataset
w
it
h
7
attr
ib
u
te
s
g
en
e
s
an
d
2
4
5
7
in
s
tan
ce
s
w
er
e
u
s
ed
,
co
n
tain
i
n
g
m
o
s
q
u
ito
g
en
e
s
f
r
o
m
2
0
1
0
to
2
0
1
2
,
T
h
e
p
r
o
f
ile
tr
an
s
cr
ip
ts
co
n
tai
n
s
A
G
A
P
0
0
3
7
1
4
,
A
G
A
P
0
0
4
7
7
9
,
C
PL
C
G
3
[
A
GA
P0
0
8
4
4
6
]
,
C
Y
P6
M
2
[
A
GA
P0
0
8
2
1
2
]
,
A
GA
P0
1
2
9
8
4
,
A
GA
P0
0
2
7
2
4
,
A
GA
P0
0
9
4
7
2
a
n
d
C
Y
P6
P3
[
A
GA
P0
0
2
8
6
5
]
,
R
NA
-
s
e
q
d
e
l
t
am
eth
r
in
-
r
e
s
i
s
t
an
t
t
r
a
n
s
c
r
i
p
t
o
m
e
d
is
t
in
ct
i
o
n
s
an
d
s
u
s
c
e
p
t
i
b
le
w
e
s
t
e
r
n
K
e
n
y
an
m
o
s
q
u
i
t
o
A
n
o
p
h
e
le
s
g
am
b
i
ae
g
en
e
s
av
a
i
la
b
l
e
d
a
ta
s
et
f
r
o
m
N
at
i
o
n
a
l
I
n
s
t
itu
t
e
o
f
H
e
a
lt
h
[
21
]
,
a
s
u
m
m
ar
y
ex
p
lan
atio
n
o
f
t
h
e
d
ataset
is
s
h
o
w
n
i
n
t
h
e
T
ab
le
1
.
T
ab
le
1
.
Data
s
et
d
escr
ip
tio
n
D
a
t
a
se
t
A
t
t
r
i
b
u
t
e
s
I
n
st
a
n
c
e
s
M
o
sq
u
i
t
o
A
n
o
p
h
e
l
e
s G
a
mb
i
a
e
7
2
4
5
7
3
.
2
.
M
e
t
ho
ds
T
h
e
ex
p
er
i
m
en
ta
l
to
o
l
u
s
ed
MA
T
L
A
B
to
an
a
l
y
ze
t
h
e
d
at
a
o
b
tain
ed
[
21
]
,
u
s
in
g
I
C
A
to
f
etc
h
late
n
t
f
e
a
t
u
r
e
s
,
a
n
d
c
a
r
r
y
o
u
t
c
l
a
s
s
i
f
i
c
a
t
i
o
n
u
s
i
n
g
e
n
s
e
m
b
l
e
a
l
g
o
r
i
t
h
m
a
p
p
r
o
a
c
h
[
2
2
]
o
n
t
h
e
M
A
T
L
A
B
t
o
o
l
e
n
v
i
r
o
n
m
e
n
t
.
3
.
2
.
1
.
I
nd
epen
dent
co
m
po
ne
nt
a
na
ly
s
is
(
I
CA)
I
C
A
is
a
v
al
u
ed
P
C
A
ex
te
n
s
io
n
th
at
h
as
r
e
m
ai
n
ed
co
n
s
er
v
ativ
e
s
in
ce
t
h
e
v
i
s
o
r
p
ar
tin
g
o
f
in
d
ep
en
d
en
t
b
a
s
es
f
r
o
m
t
h
e
ir
d
ir
ec
t
g
r
o
u
p
in
g
[
2
0
]
.
T
h
e
o
r
ig
in
al
f
ac
t
o
f
I
C
A
is
t
h
e
p
o
s
s
es
s
io
n
s
o
f
u
n
co
r
r
elatio
n
o
f
t
h
e
g
e
n
er
al
P
C
A
.
B
u
il
t
n
x
p
on
d
ata
m
ed
iu
m
X
,
w
h
o
s
e
r
o
w
s
(
=
1
…
,
)
r
ec
k
o
n
to
w
ar
d
v
ar
iab
les
o
b
s
er
v
ed
also
w
h
o
s
e
(
=
1
…
,
)
co
lu
m
n
s
ar
e
th
e
en
titi
e
s
o
f
m
atc
h
i
n
g
v
ar
iab
les,
t
h
e
I
C
A
X
m
o
d
el,
w
r
itte
n
as
f
o
llo
w
s
:
=
(
1
)
W
ith
co
m
p
lete
o
v
er
v
ie
w
,
A
is
a
f
u
s
io
n
m
a
tr
ix
,
w
h
er
e
S
is
a
is
a
b
asis
m
a
tr
ix
u
n
d
er
th
e
n
ee
d
o
f
b
ein
g
s
tati
s
tical
l
y
in
d
ep
en
d
en
t
as
co
n
ce
iv
ab
le.
I
n
d
ep
en
d
e
n
t
co
m
p
o
n
en
ts
ar
e
th
e
in
n
o
v
a
tiv
e
v
ar
iab
les
k
ep
t
in
t
h
e
r
o
w
s
o
f
S
,
to
w
it,
t
h
e
v
ar
iab
les
d
etec
ted
ar
e
li
n
ea
r
l
y
co
m
p
o
s
ed
i
n
d
ep
en
d
en
t
co
m
p
o
n
en
t
s
.
T
h
e
in
d
ep
en
d
en
t
co
m
p
o
n
e
n
ts
ac
h
iev
ed
b
y
lear
n
i
n
g
t
h
e
p
r
ec
is
e
lin
ea
r
g
r
o
u
p
i
n
g
s
o
f
t
h
e
v
ar
iab
l
es
o
b
s
er
v
ed
,
s
u
b
s
eq
u
en
t
l
y
m
i
x
in
g
ca
n
b
e
in
v
er
ted
as:
=
=
−
1
=
(
2
)
3
.
2
.
2
.
E
ns
e
m
b
le
cla
s
s
if
ier
E
n
s
e
m
b
le
class
i
f
ier
s
ca
n
b
e
p
r
o
f
icien
t
u
s
in
g
o
n
u
n
r
elate
d
s
u
b
s
ets
o
f
t
h
e
d
ata
tr
ain
in
g
,
d
iv
er
s
e
class
i
f
icatio
n
co
n
s
tr
ai
n
ts
,
o
r
with
d
i
v
er
s
e
s
u
b
s
et
f
ea
t
u
r
es
i
n
r
an
d
o
m
s
u
b
s
p
ac
e
m
o
d
el
[
2
3
].
E
n
s
e
m
b
le
cla
s
s
i
f
ier
co
m
p
r
is
e
s
o
f
in
teg
r
ati
n
g
f
a
llo
u
ts
o
f
as
s
o
r
ted
class
i
f
ier
s
to
p
r
o
d
u
ce
a
co
n
clu
d
i
n
g
d
ec
is
io
n
,
i
t
is
f
r
eq
u
en
tl
y
u
s
ed
f
o
r
g
ain
i
n
g
h
i
g
h
l
y
ac
c
u
r
ate
r
esu
lts
.
E
n
s
e
m
b
le
cla
s
s
i
f
ier
s
ar
e
r
elativ
el
y
co
m
m
o
n
i
n
m
a
c
h
i
n
e
lear
n
i
n
g
co
m
p
lica
tio
n
s
,
an
d
ca
n
b
e
e
m
p
lo
y
ed
i
n
b
io
in
f
o
r
m
atic
s
f
ield
.
C
las
s
i
f
icatio
n
d
ec
is
io
n
i
s
ac
h
iev
ed
b
y
m
er
g
in
g
th
e
d
ec
is
io
n
o
f
ea
c
h
clas
s
i
f
ier
[
2
4
]
.
E
n
s
e
m
b
le
ap
p
r
o
ac
h
es
is
m
ac
h
in
e
lear
n
i
n
g
tech
n
iq
u
e
s
c
o
m
b
i
n
es
d
ec
i
s
io
n
s
to
ad
v
an
ce
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
g
en
er
al
cla
s
s
i
f
icat
io
n
.
Se
v
er
al
ter
m
s
h
av
e
b
ee
n
d
is
co
v
e
r
ed
in
th
e
l
iter
atu
r
e
to
s
ig
n
i
f
y
co
m
p
ar
ab
le
co
n
n
o
tatio
n
s
s
u
c
h
as;
m
u
lt
i
-
s
tr
at
eg
y
lear
n
i
n
g
,
ag
g
r
eg
at
io
n
,
i
n
teg
r
at
io
n
m
u
ltip
le
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
2
,
A
p
r
il 2
0
2
1
:
1
56
1
-
1569
1564
class
i
f
ier
s
,
cla
s
s
i
f
ier
f
u
s
io
n
,
c
o
m
b
i
n
atio
n
,
co
m
m
ittee,
a
n
d
s
o
o
n
.
E
n
s
e
m
b
le
clas
s
i
f
ier
m
a
y
h
av
e
co
m
p
lete
a
n
d
i
m
p
r
o
v
ed
p
er
f
o
r
m
an
ce
t
h
a
n
d
is
cr
ete
b
ase
class
i
f
ier
s
.
T
h
e
ef
f
icien
c
y
o
f
e
n
s
e
m
b
le
ap
p
r
o
ac
h
es
is
e
x
tr
e
m
e
l
y
d
ep
en
d
en
t
o
n
th
e
u
n
co
n
v
en
t
io
n
alit
y
o
f
er
r
o
r
d
ev
o
ted
b
y
d
is
cr
ete
lear
n
er
.
E
n
s
e
m
b
le
ap
p
r
o
ac
h
es
p
er
f
o
r
m
a
n
ce
h
in
g
e
o
n
t
h
e
ac
cu
r
ac
y
an
d
v
ar
iet
y
o
f
t
h
e
b
ase
lear
n
er
s
,
e
n
s
e
m
b
le
class
i
f
icatio
n
h
as c
o
m
m
o
n
tech
n
iq
u
es;
B
o
o
s
tr
ap
ag
g
r
eg
at
in
g
(
B
ag
g
i
n
g
)
e
m
p
lo
y
s
tr
ain
in
g
th
e
d
a
ta
b
y
ar
b
itra
r
il
y
ch
a
n
g
in
g
t
h
e
u
n
iq
u
e
tr
ain
i
n
g
T
b
y
ite
m
s
N
d
ata.
T
h
e
tr
ai
n
in
g
a
u
x
iliar
y
s
et
s
ar
e
c
alled
b
o
o
ts
tr
ap
d
u
p
licates
w
it
h
s
o
m
e
o
cc
u
r
r
en
ce
s
n
o
t
ap
p
ea
r
in
g
w
h
ile
o
th
er
s
g
iv
e
t
h
e
i
m
p
r
es
s
io
n
m
o
r
e
th
an
o
n
ce
.
T
h
e
C
*
(
x
)
f
i
n
al
class
i
f
ier
is
b
u
ilt
b
y
co
m
b
i
n
i
n
g
C
i(
x
)
.
A
ll
C
i(
x
)
ta
k
es a
n
eq
u
i
v
ale
n
t d
iv
i
s
io
n
.
A
d
ap
tiv
e
b
o
o
s
ti
n
g
(
A
d
aB
o
o
s
t
)
tech
n
iq
u
e
e
f
f
ec
ts
th
e
tr
ain
i
n
g
d
ata.
Or
i
g
i
n
all
y
,
th
e
p
r
o
ce
d
u
r
e
allo
ca
tes
a
l
l
x
i
i
n
s
t
a
n
c
e
b
y
m
e
a
n
s
o
f
a
n
e
q
u
i
v
a
l
e
n
t
m
a
s
s
.
I
n
i
n
d
i
v
i
d
u
a
l
i
t
e
r
a
t
i
o
n
i
,
k
n
o
w
l
e
d
g
e
a
l
g
o
r
i
t
h
m
a
t
t
e
m
p
t
s
t
o
d
i
m
i
n
i
s
h
t
h
e
t
r
a
i
n
i
n
g
s
e
t
w
e
i
g
h
t
e
d
e
r
r
o
r
a
n
d
a
c
l
a
s
s
i
f
i
e
r
(
)
i
s
y
i
e
l
d
e
d
.
T
h
e
(
)
w
e
i
g
h
t
e
d
e
r
r
o
r
i
s
ca
l
cu
lated
an
d
u
s
e
f
u
l
i
n
in
f
o
r
m
i
n
g
th
e
tr
ain
in
g
i
n
s
ta
n
ce
s
x
i
w
ei
g
h
ts
.
w
ei
g
h
t
r
is
e
s
g
i
v
i
n
g
to
i
ts
ef
f
ec
t
s
o
n
th
e
p
er
f
o
r
m
a
n
ce
o
f
clas
s
if
ier
’
s
th
at
allo
ts
a
w
ei
g
h
t
h
ig
h
er
f
o
r
a
m
is
c
lass
if
ied
x
i
a
n
d
a
s
m
all
w
ei
g
h
t
ai
m
ed
at
a
n
ac
ce
p
tab
ly
clas
s
i
f
i
ed
x
i.
T
h
e
co
n
clu
d
in
g
class
if
ier
C
*
(
x
)
is
cr
ea
ted
b
y
a
d
is
cr
ete
C
i
(
x
)
w
ei
g
h
ted
v
o
te
r
en
d
er
in
g
to
it
s
b
u
ilt
ac
c
u
r
ac
y
o
n
th
e
tr
ai
n
in
g
w
ei
g
h
t
ed
s
et
[
1
9
]
.
A
d
o
p
tin
g
Ka
m
r
a
n
,
et
al
.
[2
0
]
,
th
e
y
s
h
o
w
ed
h
o
w
a
b
o
o
s
tin
g
alg
o
r
ith
m
w
o
r
k
s
f
o
r
d
atasets
,
th
e
n
tr
ain
ed
b
y
m
u
lti
-
m
o
d
el
d
esig
n
s
(
en
s
e
m
b
le
lear
n
in
g
)
.
T
h
ese
ad
v
an
ce
s
r
e
s
u
l
t
e
d
i
n
t
h
e
a
d
a
p
t
i
v
e
b
o
o
s
t
i
n
g
(
A
d
a
B
o
o
s
t
)
.
P
r
e
s
u
m
e
c
o
n
s
t
r
u
c
t
i
n
g
D
t
s
u
c
h
t
h
a
t
1
(
)
=
1
g
i
v
e
n
D
t
a
n
d
h
t:
+
1
{
}
=
(
)
{
−
=
ℎ
(
)
≠
ℎ
(
)
(
3
)
(
)
e
xp
(
−
ℎ
(
)
)
(
4
)
w
h
er
e
s
tates to
t
h
e
n
o
r
m
a
lizat
io
n
f
ac
to
r
an
d
is
as f
o
llo
w
s
;
=
1
2
(
1
−
∈
∈
)
(
5
)
B
asic
en
s
e
m
b
le
cla
s
s
i
f
icat
io
n
tech
n
iq
u
es
n
a
m
el
y
:
T
h
e
m
ax
v
o
tin
g
(
MV
)
,
w
ei
g
h
ted
av
er
a
g
in
g
(
W
A
)
an
d
Av
er
ag
in
g
.
Ma
x
v
o
tin
g
(
MV
)
ex
is
ts
[
2
5
-
2
7
]
E
n
s
e
m
b
le
lear
n
in
g
h
av
e
t
h
r
ee
co
m
b
i
n
atio
n
al
m
et
h
o
d
s
:
s
tac
k
i
n
g
(
ST
K)
,
b
len
d
in
g
(
B
L
D)
,
b
ag
g
i
n
g
(
B
AG)
an
d
b
o
o
s
tin
g
(
B
OT
)
[
2
8
-
3
1
].
3
.
3
.
E
v
a
lua
t
io
n per
f
o
r
m
a
nce
Data
m
i
n
i
n
g
m
o
d
el
p
er
f
o
r
m
a
n
ce
ev
alu
atio
n
r
eq
u
ir
es
m
etr
ic
s
o
f
v
alid
atio
n
s
,
clas
s
i
f
icatio
n
alg
o
r
ith
m
s
u
s
e
s
th
e
co
n
f
u
s
io
n
m
atr
i
x
i
n
an
al
y
z
in
g
f
o
u
r
f
ea
t
u
r
es
k
n
o
w
n
as
th
e;
tr
u
e
p
o
s
itiv
e
(
T
P
)
,
f
alse
p
o
s
itiv
e
(
FP
)
,
tr
u
e
n
eg
at
iv
e
(
T
N)
an
d
th
e
f
alse
n
eg
ati
v
e
(
FN)
.
T
h
ese
f
ea
tu
r
es
r
ec
o
g
n
ize
th
e
co
r
r
ec
tl
y
a
n
d
in
co
r
r
ec
tly
clas
s
i
f
ied
in
s
ta
n
ce
s
f
r
o
m
t
h
e
g
iv
e
n
s
a
m
p
le
o
f
d
ataset
u
s
ed
in
test
in
g
t
h
e
m
o
d
el
[
5
,
3
2
]
.
3
.
4
.
Appl
ica
t
io
ns
An
e
n
h
an
ce
d
p
at
h
o
f
g
en
e
ex
p
r
ess
io
n
a
n
al
y
s
i
s
i
n
d
e
n
ti
f
y
i
n
g
R
N
A
-
s
eq
d
ata
d
i
s
co
v
er
ies
f
o
r
r
elate
d
g
en
e
s
ca
n
b
e
h
elp
f
u
l
in
th
e
d
ev
elo
p
m
e
n
t
o
f
v
ar
io
u
s
ap
p
licatio
n
s
s
u
ch
as
m
o
d
i
f
ied
tr
ea
t
m
e
n
t,
d
is
ea
s
e
s
d
etec
tio
n
,
g
e
n
es
a
n
d
d
r
u
g
d
is
co
v
er
y
,
t
u
m
o
r
r
ec
o
g
n
i
tio
n
,
ai
l
m
e
n
ts
,
a
m
o
n
g
o
th
er
s
.
Data
m
in
i
n
g
tec
h
n
iq
u
e
i
s
u
s
ed
i
n
id
en
t
if
y
i
n
g
t
h
e
d
esi
g
n
s
an
d
p
o
s
s
e
s
s
es
f
a
n
ta
s
tic
ap
p
l
icab
le
alg
o
r
ith
m
s
to
o
ls
.
I
n
t
h
i
s
s
t
u
d
y
,
M
A
T
L
A
B
to
o
l
is
u
s
ed
to
ca
r
r
y
o
u
t
t
h
e
p
r
o
g
r
a
m
d
u
e
to
i
ts
u
s
er
-
f
r
ien
d
l
y
en
v
ir
o
n
m
e
n
t
[
1
6
]
,
to
p
r
ed
ict
R
N
A
-
s
eq
tech
n
o
lo
g
y
f
o
r
t
h
e
p
r
o
g
n
o
s
is
an
d
d
ialn
o
s
is
o
f
m
alar
ia
ail
m
en
ts
u
s
in
g
a
n
8
GB
R
A
M
s
ize,
6
4
-
b
it
S
y
s
te
m
,
iC
o
r
e2
p
r
o
ce
s
s
o
r
an
d
MA
T
L
A
B
2
0
1
5
A
to
o
l.
4.
RE
SU
L
T
S
A
ND
D
I
SCU
SS
I
O
NS
T
h
is
s
t
u
d
y
d
eter
m
in
e
s
R
N
A
-
s
eq
in
n
o
v
atio
n
o
f
2
4
5
7
in
s
ta
n
c
es
m
o
s
q
u
ito
es
’
d
ata.
I
C
A
al
g
o
r
ith
m
w
a
s
ap
p
lied
to
f
etch
o
u
t
laten
t
co
m
p
o
n
en
ts
f
r
o
m
th
e
a
n
o
p
h
eles
’
d
ata,
th
e
I
C
A
f
ea
tu
r
e
e
x
tr
ac
ti
o
n
d
is
tin
g
u
is
h
es
a
n
d
r
e
m
o
v
e
s
u
n
c
o
r
r
e
l
a
t
e
d
v
a
r
i
a
b
l
e
s
,
t
o
c
h
o
o
s
e
t
h
e
d
e
t
e
r
m
i
n
a
n
t
v
a
r
i
a
n
c
e
w
i
t
h
a
r
e
d
u
c
e
d
n
u
m
b
e
r
o
f
i
n
d
e
p
e
n
d
e
n
t
c
o
m
p
o
n
e
n
t
s
t
o
g
i
v
e
i
m
p
o
r
t
a
n
t
u
s
e
f
u
l
g
e
n
e
e
v
i
d
e
n
c
e
v
a
l
u
a
b
l
e
f
o
r
s
u
p
p
l
em
e
n
t
a
r
y
e
x
a
m
i
n
a
t
i
o
n
.
E
n
s
e
m
b
l
e
A
d
a
B
o
o
s
t
class
i
f
icatio
n
alg
o
r
it
h
m
i
s
ap
p
lied
o
n
th
e
ex
tr
ac
ted
I
C
A
4
5
laten
t
s
i
g
n
i
f
ica
n
t
f
ea
tu
r
e
s
o
f
g
e
n
es
r
ea
lized
i
n
7
.
8
4
8
6
Seco
n
d
s
.
10
-
f
o
ld
s
cr
o
s
s
v
alid
atio
n
is
u
s
ed
to
ev
al
u
at
e
th
e
cla
s
s
i
f
icatio
n
ex
ec
u
tio
n
p
er
f
o
r
m
a
n
ce
,
u
s
i
n
g
0
.
0
5
p
ar
am
eter
h
o
ld
o
u
t to
tr
ai
n
in
g
t
h
e
d
ata
an
d
5
% f
o
r
test
i
n
g
th
e
cla
s
s
i
f
icat
io
n
ac
cu
r
ac
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
I
C
A
-
en
s
emb
le
lea
r
n
in
g
a
p
p
r
o
a
ch
es fo
r
p
r
ed
ictio
n
o
f
…
(
Mich
ea
l O
la
o
lu
A
r
o
w
o
lo
)
1565
Ass
es
s
m
en
t
lear
n
in
g
p
r
o
ce
d
u
r
e
class
if
ica
tio
n
is
u
s
ed
to
tr
ai
n
,
test
a
n
d
e
v
al
u
ate
t
h
e
ex
p
er
i
m
en
t
u
s
i
n
g
a
1
0
-
f
o
ld
cr
o
s
s
v
alid
atio
n
in
eli
m
i
n
ati
n
g
t
h
e
s
a
m
p
li
n
g
p
ar
t
ialities
.
R
es
u
lt
e
v
al
u
atio
n
is
ca
r
r
ied
o
u
t
o
n
th
e
co
m
p
u
tatio
n
al
ti
m
e
an
d
p
er
f
o
r
m
an
ce
m
etr
ics
[
32
]
.
C
las
s
if
i
ca
tio
n
o
f
t
h
e
m
o
d
els,
u
s
i
n
g
A
d
a
B
o
o
s
t
en
s
e
m
b
le
class
i
f
ier
i
s
ca
r
r
ied
o
u
t
w
i
th
9
3
.
3
%
p
er
f
o
r
m
an
ce
ac
cu
r
ac
y
.
T
h
e
r
es
u
lt
s
a
n
d
p
r
o
ce
d
u
r
es
ar
e
s
h
o
w
n
i
n
th
e
f
i
g
u
r
es
b
elo
w
.
I
C
A
f
ea
t
u
r
e
ex
tr
ac
tio
n
al
g
o
r
ith
m
i
s
u
s
ed
to
ex
tr
ac
t
th
e
h
id
d
en
f
ea
tu
r
es
f
r
o
m
m
o
s
q
u
ito
an
o
p
h
eles
d
ata
s
h
o
w
n
i
n
Fi
g
u
r
e
2
.
T
h
e
ex
tr
ac
ted
f
ea
tu
r
es
ar
e
cl
ass
i
f
ied
u
s
i
n
g
en
s
e
m
b
le
alg
o
r
it
h
m
,
t
h
e
s
ca
tter
ed
p
lo
t
an
d
r
esu
lt
s
ar
e
s
h
o
w
n
in
th
e
f
i
g
u
r
es
b
elo
w
u
s
in
g
t
h
e
co
n
f
u
s
io
n
m
atr
i
x
to
g
iv
e
a
r
esu
lt
to
th
e
p
er
f
o
r
m
a
n
ce
m
etr
ics.
I
n
Fi
g
u
r
e
3
a
s
ca
tter
ed
p
lo
t
is
s
h
o
w
n
f
o
r
t
h
e
class
if
icatio
n
,
t
h
e
co
r
r
ec
tl
y
clas
s
i
f
ied
an
d
m
i
s
class
if
ied
u
s
i
n
g
d
o
ts
an
d
cr
o
s
s
s
i
g
n
s
to
r
ep
r
esen
t
v
al
u
es
f
o
r
th
e
v
ar
ia
b
les,
in
d
icati
n
g
v
al
u
es
f
o
r
in
d
iv
id
u
al
d
ata
p
o
in
t
s
,
th
is
p
lo
t
is
u
s
ed
in
o
b
s
er
v
i
n
g
t
h
e
r
elatio
n
s
h
ip
s
b
et
w
ee
n
th
e
c
lass
i
f
ied
v
ar
iab
les
.
Fi
g
u
r
e
4
a
n
d
Fig
u
r
e
5
s
h
o
w
s
th
e
co
n
f
u
s
io
n
m
atr
i
x
f
o
r
t
h
e
cl
ass
i
f
icatio
n
s
o
f
t
h
e
ex
p
er
i
m
e
n
t,
u
s
i
n
g
b
a
g
g
ed
a
n
d
b
o
o
s
ted
e
n
s
e
m
b
le
clas
s
i
f
ier
s
.
T
h
e
co
n
f
u
s
io
n
m
atr
ix
tab
le
i
s
th
en
u
s
ed
i
n
d
escr
ib
in
g
th
e
p
e
r
f
o
r
m
an
ce
o
f
th
e
cla
s
s
i
f
icatio
n
m
o
d
el
o
f
t
h
e
s
et
s
o
f
th
e
tes
ted
d
ata
w
it
h
th
e
k
n
o
w
n
tr
u
e
v
al
u
e
s
w
ith
t
h
e
co
n
f
u
s
io
n
m
atr
i
x
r
ep
r
esen
ted
w
it
h
tr
u
e
p
o
s
itiv
e,
f
a
ls
e
p
o
s
itiv
e,
tr
u
e
n
eg
at
iv
e
a
n
d
f
al
s
e
n
eg
ati
v
e
v
alu
e
s
.
Fig
u
r
e
2
.
T
h
e
m
o
s
q
u
ito
an
o
p
h
eles g
a
m
b
iae
d
ataset
Fig
u
r
e
3
.
C
lass
if
ica
tio
n
s
ca
t
ter
ed
p
lo
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
2
,
A
p
r
il 2
0
2
1
:
1
56
1
-
1569
1566
Fig
u
r
e
4
.
C
o
n
f
u
s
io
n
m
atr
ix
f
o
r
en
s
e
m
b
le
s
u
b
s
p
ac
e
d
is
cr
i
m
i
n
an
t c
las
s
i
f
icatio
n
T
P
=3
8
;
T
N=
1
8
; FP
=3
FN=1
Fig
u
r
e
5
.
C
o
n
f
u
s
io
n
m
atr
ix
f
o
r
en
s
e
m
b
le
b
ag
g
ed
tr
ee
class
if
icatio
n
T
P
=3
5
;
T
N=
1
4
; F
P
=7
;
FN=4
T
esti
n
g
t
h
e
d
ata
m
i
n
i
n
g
lear
n
in
g
p
er
f
o
r
m
a
n
ce
m
et
h
o
d
s
,
th
e
R
N
A
-
s
eq
d
ata
w
as
co
p
ied
f
r
o
m
t
h
e
h
ttp
s
:/
/f
i
g
s
h
ar
e.
co
m
/ar
tic
les/
Ad
d
itio
n
al_
f
ile_
4
_
o
f
_
R
N
Aseq
_
an
al
y
s
es_
o
f
_
c
h
an
g
e
s
_
in
_
th
e_
An
o
p
h
eles_
g
a
m
b
i
ae
_
tr
an
s
cr
ip
to
m
e_
ass
o
ciate
d
_
w
it
h
_
r
esi
s
tan
ce
_
to
_
p
y
r
eth
r
o
id
s
_
in
_
Ken
y
a_
id
en
t
if
ica
tio
n
_
o
f
_
ca
n
d
id
ater
esis
ta
n
c
e_
g
en
es_
a
n
d
_
ca
n
d
id
ater
esis
ta
n
ce
_
SNP
s
/4
3
4
6
2
7
9
/1
r
e
p
o
s
ito
r
y
.
I
C
A
f
ea
t
u
r
e
ex
tr
ac
tio
n
t
ec
h
n
iq
u
e
w
as
u
s
ed
o
n
th
e
2
4
5
7
g
en
e
s
f
ea
tu
r
es,
a
n
d
ex
tr
ac
ted
1
5
7
2
f
ea
tu
r
es
w
it
h
4
5
laten
t c
o
m
p
o
n
e
n
ts
.
E
n
s
e
m
b
le
class
i
f
icatio
n
is
u
s
ed
to
p
r
ed
ict
th
e
p
er
f
o
r
m
a
n
ce
.
R
es
u
lt
d
e
m
o
n
s
tr
ated
th
e
e
f
f
icie
n
c
y
o
f
d
ata
m
i
n
in
g
ap
p
r
o
ac
h
ed
in
g
e
n
es.
T
h
e
p
er
f
o
r
m
a
n
ce
r
es
u
lt
s
f
o
r
t
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
ar
e
r
ev
ea
led
an
d
r
elate
d
in
T
ab
le
2
.
T
h
e
o
u
tco
m
e
s
h
o
w
s
t
h
a
t
S
u
b
s
p
a
c
e
D
i
s
c
r
i
m
i
n
a
n
t
e
n
s
e
m
b
l
e
c
l
a
s
s
i
f
i
c
a
t
i
o
n
o
u
t
p
e
r
f
o
r
m
s
b
a
g
g
e
d
t
r
e
e
e
n
s
e
m
b
l
e
i
n
t
e
r
m
s
o
f
a
c
c
u
r
a
c
y
.
I
n
t
h
is
s
t
u
d
y
,
a
n
i
m
p
r
o
v
ed
i
n
v
esti
g
atio
n
o
f
t
h
e
cla
s
s
i
f
icati
o
n
o
f
m
alar
ia
v
ec
to
r
d
ata
is
ca
r
r
ied
o
u
t,
n
u
m
e
r
o
u
s
w
o
r
k
s
h
a
v
e
b
e
e
n
p
r
o
p
o
s
e
d
b
y
i
n
v
e
s
t
i
g
a
t
o
r
s
,
t
h
e
f
i
g
u
r
e
a
n
d
t
a
b
l
e
s
a
b
o
v
e
h
a
v
e
s
h
o
w
n
a
n
d
d
e
m
o
n
s
tr
ated
th
at,
d
i
m
e
n
s
io
n
alit
y
r
ed
u
cti
o
n
m
o
d
el
w
ith
I
C
A
f
ea
t
u
r
e
ex
tr
ac
tio
n
m
eth
o
d
s
ca
n
p
r
o
g
r
ess
en
s
e
m
b
le
class
i
f
icatio
n
r
esu
lts
,
Fi
g
u
r
e
6
s
h
o
w
s
t
h
e
p
er
f
o
r
m
a
n
ce
ch
ar
t
f
o
r
co
m
p
ar
i
n
g
t
h
e
o
u
tp
u
t
r
esu
lts
.
T
h
i
s
s
t
u
d
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
I
C
A
-
en
s
emb
le
lea
r
n
in
g
a
p
p
r
o
a
ch
es fo
r
p
r
ed
ictio
n
o
f
…
(
Mich
ea
l O
la
o
lu
A
r
o
w
o
lo
)
1567
p
r
o
p
o
s
e
d
a
p
r
e
d
i
c
t
i
o
n
a
n
d
d
e
t
e
c
t
i
o
n
m
o
d
e
l
f
o
r
m
a
l
a
r
i
a
d
i
s
e
a
s
e
i
n
h
u
m
a
n
.
T
h
e
p
r
o
p
o
s
e
d
m
e
t
h
o
d
u
s
e
d
a
n
I
C
A
d
i
m
e
n
s
i
o
n
a
l
i
t
y
r
e
d
u
c
t
i
o
n
a
n
d
e
n
s
e
m
b
l
e
c
l
a
s
s
i
f
i
c
a
t
i
o
n
d
a
t
a
m
i
n
i
n
g
p
r
o
c
e
d
u
r
e
s
,
t
h
e
in
v
es
tig
at
io
n
an
d
p
er
f
o
r
m
a
n
c
e
ass
es
s
m
en
t o
f
th
e
r
es
u
lt
s
g
o
tte
n
w
er
e
s
h
o
w
n
i
n
th
e
ta
b
les a
n
d
f
ig
u
r
es
b
elo
w
.
T
ab
le
2
.
P
er
f
o
r
m
a
n
ce
m
etr
ic
s
tab
le
f
o
r
th
e
co
n
f
u
s
io
n
m
atr
i
x
P
e
r
f
o
r
man
c
e
M
e
t
r
i
c
s
En
se
mb
l
e
S
u
b
sp
a
c
e
D
i
s
c
r
i
m
i
n
a
n
t
C
l
a
ssi
f
i
c
a
t
i
on
En
se
mb
l
e
B
a
g
g
e
d
T
r
e
e
C
l
a
ssi
f
i
c
a
t
i
o
n
A
c
c
u
r
a
c
y
(
%)
9
3
.
3
8
1
.
7
S
e
n
si
t
i
v
i
t
y
(
%)
9
7
.
4
8
9
.
7
S
p
e
c
i
f
i
c
i
t
y
(
%)
8
5
.
7
6
6
.
7
P
r
e
c
i
si
o
n
(
%)
9
2
.
7
8
3
.
3
R
e
c
a
l
l
(
%)
9
7
.
4
8
9
.
7
F
-
S
c
o
r
e
(
%)
9
5
.
0
8
6
.
4
Fig
u
r
e
6
.
P
er
f
o
r
m
a
n
ce
m
etr
ic
s
g
r
ap
h
5.
CO
NCLU
SI
O
N
An
en
h
a
n
ce
d
class
i
f
icatio
n
a
p
p
r
o
ac
h
f
o
r
m
alar
ia
p
r
o
g
n
o
s
is
an
d
d
iag
n
o
s
is
u
s
i
n
g
d
i
m
e
n
s
io
n
alit
y
r
ed
u
ctio
n
a
n
d
clas
s
i
f
icatio
n
a
lg
o
r
ith
m
w
as
p
r
o
p
o
s
ed
,
n
u
m
e
r
o
u
s
w
o
r
k
s
b
y
r
esear
ch
er
s
in
th
is
ar
ea
h
a
s
b
ee
n
r
ev
i
w
ed
,
r
esu
lt
s
o
f
th
e
e
x
p
er
im
en
t
h
a
v
e
d
e
m
o
n
s
tr
ated
I
C
A
f
ea
tu
r
e
ex
tr
ac
tio
n
d
i
m
e
n
s
io
n
alit
y
r
ed
u
ctio
n
ca
n
s
u
p
p
o
r
t
th
e
ad
v
an
ce
m
e
n
t
o
f
e
n
s
e
m
b
le
cla
s
s
i
f
icatio
n
.
R
ec
en
t
an
d
f
u
tu
r
e
w
o
r
k
s
ca
n
b
e
e
n
h
an
ce
d
u
s
in
g
o
t
h
er
en
s
e
m
b
le
clas
s
if
ier
s
w
it
h
o
th
e
r
f
ea
tu
r
e
ex
tr
ac
tio
n
al
g
o
r
ith
m
s
.
RE
F
E
R
E
NC
E
S
[1
]
S
h
a
n
w
e
n
S
.
,
C
h
u
n
y
u
W
.
,
Hu
i
D.
,
Qu
a
n
Z
.
,
“
M
a
c
h
i
n
e
L
e
a
rn
in
g
a
n
d
i
ts
A
p
p
li
c
a
ti
o
n
s
in
P
lan
t
M
o
l
e
c
u
lar
S
tu
d
ies
,
”
Briefin
g
s i
n
F
u
n
c
ti
o
n
a
l
Ge
n
o
mic
s
,
v
o
l.
1
9
,
n
o
.
1
,
p
p
.
4
0
-
48
,
2
0
1
9
.
[2
]
Da
v
id
F
.
R.
,
Ka
te
C.
,
Ya
n
k
Y.
L
.
,
Ka
rin
e
G
.
,
Ro
c
h
L
.
,
“
P
re
d
ictin
g
G
e
n
e
Ex
p
re
ss
io
n
in
t
h
e
Hu
m
a
n
M
a
laria
P
a
ra
site
P
las
m
o
d
iu
m
F
a
lcip
a
ru
m
Us
in
g
Histo
n
e
M
o
d
if
ica
ti
o
n
,
Nu
c
leo
so
m
e
P
o
sit
io
n
in
g
,
a
n
d
3
D
L
o
c
a
li
z
a
ti
o
n
F
e
a
tu
re
s,”
PL
OS
C
o
mp
u
ta
ti
o
n
a
l
B
io
l
o
g
y
,
v
o
l.
1
5
,
n
o
.
9
,
p
p
.
1
-
2
3
,
2
0
1
9
.
[3
]
A
li
sta
ir
M
il
e
s
,
e
t
a
l.
,
“
G
e
n
e
ti
c
d
iv
e
rsity
o
f
th
e
Af
rica
n
m
a
laria
v
e
c
to
r
A
n
o
p
h
e
les
g
a
m
b
iae
,
”
N
a
tu
re
,
v
o
l.
5
5
2
,
no
.
7
6
8
3
,
p
p
.
9
6
-
1
0
0
,
2
0
1
7
.
[
4
]
A
r
o
w
o
l
o
M
.
O
.
,
A
d
e
b
i
y
i
M
.
,
A
d
e
b
i
y
i
A
.
,
“
A
D
i
m
e
n
s
i
o
n
a
l
R
e
d
u
c
e
d
M
o
d
e
l
f
o
r
t
h
e
C
l
a
s
s
i
f
i
c
a
t
i
o
n
o
f
R
N
A
-
s
e
q
A
n
o
p
h
e
l
e
s
G
a
m
b
i
a
e
D
a
t
a
,
”
J
o
u
r
n
a
l
o
f
T
h
e
o
r
e
t
i
c
a
l
a
n
d
A
p
p
l
i
e
d
I
n
f
o
r
m
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
9
7
,
n
o
.
2
3
,
p
p
.
3
4
8
7
-
3
4
9
6
,
2
0
1
9
.
[5
]
Ka
rth
ik
S
.
a
n
d
S
u
d
h
a
M
.
,
“
A
S
u
rv
e
y
o
n
M
a
c
h
in
e
L
e
a
rn
in
g
A
p
p
ro
a
c
h
e
s
in
G
e
n
e
Ex
p
re
ss
io
n
Cl
a
ss
if
i
c
a
ti
o
n
in
M
o
d
e
ll
i
n
g
Co
m
p
u
tatio
n
a
l
Dia
g
n
o
stic
S
y
ste
m
f
o
r
Co
m
p
lex
Dis
e
a
se
s,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
E
n
g
i
n
e
e
rin
g
a
n
d
Ad
v
a
n
c
e
d
T
e
c
h
n
o
lo
g
y
,
v
o
l
.
8
,
n
o
.
2
,
p
p
.
1
8
2
-
1
9
1
,
2
0
1
8
.
[
6
]
J
o
h
n
s
o
n
N
.
T
.
,
D
h
r
o
s
o
A
.
,
H
u
g
h
e
s
K
.
J
.
,
K
o
r
k
i
n
D
.
,
“
B
i
o
l
o
g
i
c
a
l
c
l
a
s
s
i
f
i
c
a
t
i
o
n
w
i
t
h
R
N
A
-
s
e
q
d
a
t
a
:
C
a
n
a
l
t
e
r
n
a
t
i
v
e
l
y
s
p
l
i
c
e
d
t
r
a
n
s
c
r
i
p
t
e
x
p
r
e
s
s
i
o
n
e
n
h
a
n
c
e
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
c
l
a
s
s
i
f
i
e
r
s
?
,
”
R
N
A
,
v
o
l
.
2
4
,
n
o
.
9
,
p
p
.
1
1
1
9
-
1
1
3
2
,
2
0
1
8
.
[7
]
L
ib
b
re
c
h
t
M
.
W
.
a
n
d
No
b
le
W
.
S
.
,
“
M
a
c
h
i
n
e
lea
rn
in
g
a
p
p
li
c
a
ti
o
n
s
i
n
g
e
n
e
ti
c
s
a
n
d
g
e
n
o
m
ics
,
”
Na
tu
re
Rev
iews
Ge
n
e
ti
c
s
,
v
o
l.
1
6
,
p
p
.
3
2
1
-
3
3
2
,
2
0
1
5
.
[8
]
Ja
g
g
a
Z.
a
n
d
G
u
p
ta
D.,
“
Clas
si
f
ica
ti
o
n
m
o
d
e
ls
f
o
r
c
lea
r
c
e
ll
re
n
a
l
c
a
rc
in
o
m
a
sta
g
e
p
ro
g
re
ss
io
n
,
b
a
se
d
o
n
tu
m
o
r
RNA
se
q
e
x
p
re
ss
io
n
train
e
d
s
u
p
e
r
v
ise
d
m
a
c
h
in
e
lea
rn
in
g
a
lg
o
rit
h
m
s,”
BM
C
Pro
c
e
e
d
in
g
s
,
v
o
l.
8
,
2
0
1
4
,
p
p
.
1
-
7
.
[9
]
Oh
D.
H.,
Kim
I.
B.
,
Kim
S
.
H.,
A
h
n
D.
H.,
“
P
re
d
ictin
g
A
u
ti
sm
S
p
e
c
tru
m
Diso
rd
e
r
Us
i
n
g
Blo
o
d
-
b
a
se
d
G
e
n
e
E
x
p
r
e
s
s
i
o
n
S
i
g
n
a
t
u
r
e
s
a
n
d
M
a
c
h
i
n
e
L
e
a
r
n
i
n
g
,
”
C
l
i
n
P
s
y
c
h
o
p
h
a
r
m
a
c
o
l
o
g
y
N
e
u
r
o
s
c
i
e
n
c
e
,
v
o
l
.
1
5
,
n
o
.
1
,
p
p
.
4
7
-
5
2
,
2
0
1
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
2
,
A
p
r
il 2
0
2
1
:
1
56
1
-
1569
1568
[1
0
]
Re
n
Q.,
A
n
ju
n
M
.
,
Qin
M
.
,
Qu
a
n
Z.
,
“
Clu
ste
rin
g
a
n
d
Clas
sif
ic
a
ti
o
n
M
e
th
o
d
s
f
o
r
S
in
g
le
-
c
e
ll
RNA
-
se
q
Da
ta,
”
Briefin
g
s i
n
Bi
o
i
n
fo
rm
a
ti
c
s
,
v
o
l.
2
1
,
n
o
.
4
,
p
p
.
1
-
1
3
,
2
0
1
9
.
[1
1
]
S
tep
h
e
n
W
.
a
n
d
R
u
h
o
ll
a
h
S
.
,
“
Us
in
g
S
u
p
e
rv
ise
d
L
e
a
rn
in
g
M
e
th
o
d
s
f
o
r
G
e
n
e
S
e
lec
ti
o
n
in
RNA
-
se
q
Ca
se
-
Co
n
tr
o
l
S
tu
d
ies
,
”
Fro
n
t
ier
s in
,
v
o
l.
9
,
n
o
.
2
9
7
,
p
p
.
1
-
6
,
2
0
1
8
.
[1
2
]
A
lq
u
icira
-
He
rn
a
n
d
e
z
,
J.,
S
a
th
e
,
A
.
,
Ji,
H.
P
.
,
Nq
u
y
e
n
Q.,
P
o
w
e
ll
J.
E.
,
“
sc
P
re
d
:
A
c
c
u
ra
te
S
u
p
e
rv
i
se
d
M
e
th
o
d
f
o
r
Ce
ll
-
ty
p
e
Clas
si
f
ic
a
ti
o
n
f
ro
m
S
in
g
le
-
c
e
ll
RN
A
-
se
q
Da
ta,”
G
e
n
o
me
Bi
o
l
o
g
y
,
v
o
l.
2
0
,
n
o
.
2
6
4
,
p
p
.
1
-
1
7
,
2
0
1
9
.
[1
3
]
Cu
i
S
.
,
W
u
Q.,
W
e
st
J.,
Ba
i
J.,
“
M
a
c
h
in
e
L
e
a
rn
in
g
-
b
a
se
d
M
icro
a
rra
y
A
n
a
l
y
se
s
In
d
ica
t
e
L
o
w
-
Ex
p
re
ss
io
n
G
e
n
e
s
M
ig
h
t
Co
ll
e
c
ti
v
e
l
y
In
f
lu
e
n
c
e
P
A
H Dise
a
se
,
”
PL
OS
Co
mp
u
ta
ti
o
n
a
l
Bi
o
l
o
g
y
,
v
o
l.
1
5
,
n
o
.
8
,
p
p
.
1
-
2
5
,
2
0
1
9
.
[1
4
]
S
h
o
n
H.
S
.
,
Yi
Y.
G
.
,
Kim
K.
O.,
Ch
a
E.
J.,
Kim
K.
A
.
,
“
Clas
si
f
ic
a
ti
o
n
o
f
S
to
m
a
c
h
Ca
n
a
c
e
r
G
e
n
e
Ex
p
re
ss
io
n
Da
ta
U
s
i
n
g
C
N
N
A
l
g
o
r
i
t
h
m
o
f
D
e
e
p
L
e
a
r
n
i
n
g
,
”
J
o
u
r
n
a
l
o
f
B
i
o
m
e
d
i
c
a
l
T
r
a
n
s
l
a
t
i
o
n
R
e
s
e
a
r
c
h
,
v
o
l
.
2
0
,
n
o
.
1
,
p
p
.
1
5
-
2
0
,
2
0
1
9
.
[1
5
]
A
d
a
m
J.
R.
,
e
t
a
l.
,
“
S
in
g
le
-
c
e
ll
R
NA
-
se
q
re
v
e
a
ls h
id
d
e
n
tran
sc
rip
t
i
o
n
a
l
v
a
riatio
n
i
n
m
a
laria
p
a
ra
sites
,
”
e
L
IFE
,
T
o
o
ls
a
n
d
Res
o
u
rc
e
s
,
v
o
l.
7
,
p
p
.
1
-
2
9
,
2
0
1
8
.
[1
6
]
T
a
n
A
.
C.
a
n
d
G
il
b
e
rt
D.,
“
En
s
e
m
b
le
M
a
c
h
in
e
L
e
a
rn
in
g
o
n
G
e
n
e
Ex
p
re
ss
io
n
Da
ta
f
o
r
Ca
n
c
e
r
Clas
sif
ic
a
ti
o
n
,
”
Ap
p
li
e
d
Bi
o
in
fo
rm
a
t
ics
,
v
o
l.
2
,
n
o
.
3
,
p
p
.
7
5
-
8
3
,
2
0
0
3
.
[1
7
]
S
o
n
g
N.,
W
a
n
g
K.,
X
u
M
.
,
X
ie
X
.
,
Ch
e
n
G
.
,
W
a
n
g
Y.,
“
De
sig
n
a
n
d
A
n
a
ly
sis
o
f
En
se
m
b
le
Clas
sif
ier
f
o
r
G
e
n
e
Ex
p
re
ss
io
n
Da
ta o
f
Ca
n
c
e
r,
”
Ad
v
a
n
c
e
me
n
t
i
n
Ge
n
e
ti
c
En
g
i
n
e
e
rin
g
,
v
o
l.
5
,
n
o
.
1
,
p
p
.
1
-
7
,
2
0
1
6
.
[1
8
]
T
a
re
k
S
.
,
El
w
a
h
a
b
R.
A
.
,
S
h
o
m
a
n
M
.
,
“
G
e
n
e
Ex
p
re
s
sio
n
Ba
se
d
Ca
n
c
e
r
Clas
si
f
ica
ti
o
n
,
”
Eg
y
p
ti
a
n
I
n
fo
rm
a
ti
c
s
J
o
u
rn
a
l
,
v
o
l
.
1
8
,
n
o
.
3
,
p
p
.
1
5
1
-
1
5
9
,
2
0
1
7
.
[1
9
]
L
i
K.,
Zh
o
u
,
G
.
,
Z
h
a
i,
J.,
L
i
F
.
,
S
h
a
o
M
.
,
“
Im
p
ro
v
e
d
P
S
O
_
A
d
a
Bo
o
st
E
n
se
m
b
le
A
l
g
o
rit
h
m
f
o
r
I
m
b
a
lan
c
e
d
Da
ta,”
S
e
n
so
rs
,
v
o
l
.
1
9
,
n
o
.
6
,
p
p
.
1
-
18
,
2
0
1
9
.
[2
0
]
Ka
m
ra
n
K.,
Kia
n
a
J.
M
.
,
M
o
jt
a
b
a
H.,
S
a
n
jan
a
M
.
,
L
a
u
ra
B.
,
Do
n
a
ld
B.
,
“
T
e
x
t
Cla
ss
i
f
ica
ti
o
n
A
lg
o
rit
h
m
s:
A
S
u
rv
e
y
,
”
In
fo
rm
a
ti
o
n
M
DPI
,
v
o
l.
1
0
,
n
o
.
1
5
0
,
p
p
.
2
-
6
8
,
2
0
1
9
[2
1
]
M
a
rian
g
e
la
B.
,
Eri
c
O.,
W
il
li
a
m
A
.
D.,
M
o
n
ica
B.
,
Ya
w
A
.
,
G
u
o
fa
Z.
,
Jo
sh
u
a
H.,
M
in
g
L
.
,
Ji
a
b
a
o
X
.
,
A
n
d
re
w
G
.
,
Jo
se
p
h
F
.
,
G
u
iy
u
n
Y.,
“
RNA
-
se
q
a
n
a
ly
se
s
o
f
c
h
a
n
g
e
s
in
th
e
A
n
o
p
h
e
les
g
a
m
b
iae
tran
sc
rip
to
m
e
a
ss
o
c
iate
d
w
it
h
re
sista
n
c
e
to
p
y
re
th
ro
id
s
i
n
Ke
n
y
a
:
id
e
n
ti
f
ica
ti
o
n
o
f
c
a
n
d
id
a
te
-
re
sista
n
c
e
g
e
n
e
s
a
n
d
c
a
n
d
id
a
te
-
re
s
istan
c
e
S
NP
s
,
”
Pa
ra
sites
a
n
d
Vec
to
r
,
v
o
l
.
8
,
n
o
.
4
7
4
,
p
p
.
1
-
1
3
,
2
0
1
5
.
[2
2
]
Ja
m
e
s
G
.
,
W
it
ten
D.,
Ha
stie
T
.
,
T
ib
sh
iran
i
R.
,
“
A
n
in
tro
d
u
c
ti
o
n
t
o
sta
ti
stica
l
lea
rn
in
g
w
it
h
a
p
p
li
c
a
ti
o
n
in
R
,
”
Ne
w
Y
o
rk
(
NY
):
S
p
ri
n
g
e
r
,
2
0
1
3
.
[2
3
]
Na
g
i
S
.
a
n
d
Bh
a
tt
a
c
h
a
ry
y
a
D.
K.,
“
Clas
si
f
ica
ti
o
n
o
f
M
icro
a
rra
y
Ca
n
c
e
r
Da
ta
Us
in
g
En
se
m
b
le
A
p
p
ro
a
c
h
,
”
Ne
two
r
k
M
o
d
e
li
n
g
An
a
lys
is i
n
He
a
lt
h
In
fo
rm
a
ti
c
s a
n
d
Bi
o
in
fo
rm
a
t
ics
,
v
o
l.
2
,
p
p
.
1
5
9
-
1
7
3
,
2
0
1
3
.
[2
4
]
S
a
ra
h
M
.
,
A
h
m
e
d
I.
S
.
,
L
a
b
ib
M
.
L
.
,
“
Clas
si
f
ic
a
ti
o
n
T
e
c
h
n
iq
u
e
s
in
G
e
n
e
Ex
p
re
ss
io
n
M
icro
a
rra
y
D
a
ta,”
In
ter
n
a
t
io
n
a
l
jo
u
rn
a
l
o
f
Co
mp
u
te
r S
c
ien
c
e
M
o
b
il
e
C
o
mp
u
ti
n
g
,
v
o
l
.
7
,
n
o
.
1
1
,
p
p
.
5
2
-
5
6
,
2
0
1
8
[2
5
]
G
u
z
m
a
n
E.
,
El
-
h
a
lab
y
M
.
,
Bru
e
g
g
e
B.
,
“
En
se
m
b
le
M
e
th
o
d
s
f
o
r
A
p
p
Re
v
ie
w
Clas
si
f
ic
a
ti
o
n
:
A
n
A
p
p
ro
a
c
h
f
o
r
S
o
f
tw
a
r
e
Ev
o
lu
ti
o
n
,
”
2
0
1
5
3
0
th
IEE
E/
ACM
I
n
t.
Co
n
fer
e
n
c
e
o
f
Au
to
m
a
ti
v
e
S
o
ft
w
a
re
En
g
in
e
e
rin
g
,
L
in
c
o
l
n
,
NE,
p
p
.
7
7
1
-
7
7
6
,
2
0
1
5
.
[2
6
]
Re
n
Y.,
S
u
g
a
n
t
h
a
n
P
.
N.,
S
r
ik
a
n
th
N.,
“
En
se
m
b
le
m
e
th
o
d
s
f
o
r
w
in
d
a
n
d
so
lar
p
o
w
e
r
f
o
re
c
a
stin
g
:
A
sta
t
e
-
o
f
th
e
-
a
rt
re
v
ie
w
,
”
Ren
e
we
a
b
le S
u
sta
in
a
b
le
En
e
rg
y
Rev
o
l
u
ti
o
n
,
v
o
l.
5
0
,
p
p
.
8
2
-
9
1
,
2
0
1
5
.
[2
7
]
F
len
n
e
rh
a
g
S
.
,
“
M
a
c
h
i
n
e
L
e
a
rn
in
g
En
se
m
b
le,”
2
0
1
7
.
[
On
l
in
e
]
.
A
v
a
il
a
b
le:
h
tt
p
:/
/f
len
n
e
rh
a
g
.
c
o
m
/2
0
1
7
-
04
-
18
-
in
tro
d
u
c
ti
o
n
-
to
-
e
n
se
m
b
les
/
,
[2
8
]
T
sa
i
C.
F
.
,
Hs
u
Y.
F
.
,
Ye
n
D.
C.
,
“
A
c
o
m
p
a
ra
ti
v
e
stu
d
y
o
f
c
las
sif
ier
e
n
se
m
b
les
f
o
r
b
a
n
k
ru
p
t
c
y
p
re
d
ictio
n
,
”
Ap
p
li
c
a
ti
o
n
S
o
f
t
Co
m
p
u
ti
n
g
J
o
u
r
n
a
l
,
n
o
.
2
4
,
p
p
.
9
7
7
-
9
8
4
,
2
0
1
4
.
[2
9
]
M
a
y
r
A
.
,
Bin
d
e
r
A
.
,
G
e
f
e
ll
e
r
O.,
S
c
h
m
id
M
.
,
“
T
h
e
Ev
o
lu
ti
o
n
o
f
Bo
o
sti
n
g
A
l
g
o
rit
h
m
s
f
ro
m
M
a
c
h
in
e
L
e
a
rn
in
g
to
S
tatisti
c
a
l
M
o
d
e
ll
in
g
,
”
M
e
th
o
d
s I
n
fo
rm
a
t
ics
a
n
d
M
e
d
icin
e
,
v
o
l.
5
3
,
n
o
.
6
,
p
p
.
4
1
9
-
4
2
7
,
2
0
1
4
.
[3
0
]
Nisio
ti
A
.
,
M
y
lo
n
a
s
A
.
,
Yo
o
P
.
D.,
M
e
m
b
e
r
S
.
,
Ka
to
s
V
.
,
“
F
r
o
m
In
tru
sio
n
De
tec
ti
o
n
t
o
A
tt
a
c
k
e
r
A
tt
rib
u
ti
o
n
:
A
Co
m
p
re
h
e
n
siv
e
S
u
rv
e
y
o
f
Un
su
p
e
rv
ise
d
M
e
th
o
d
s,”
IE
EE
C
o
mm
u
n
ica
ti
o
n
s
S
u
rv
e
y
s
&
T
u
to
ria
ls
,
v
o
l.
2
0
,
n
o
.
4
,
p
p
.
3
3
6
9
-
3
3
8
8
,
2
0
1
8
.
[3
1
]
Ha
f
i
z
a
h
S
.
,
A
riff
in
S
.
,
M
u
a
z
z
a
h
N.,
L
a
ti
ff
A
.
,
Kh
a
iri
M
.
H.
H.,
A
ri
ff
in
S
.
H.
S
.
,
e
t
a
l.
,
“
A
Re
v
i
e
w
o
f
A
n
o
m
a
l
y
De
tec
ti
o
n
T
e
c
h
n
iq
u
e
s
a
n
d
Dist
rib
u
te
d
De
n
ial
o
f
S
e
rv
ice
(D
Do
S
)
o
n
S
o
f
twa
re
De
f
in
e
d
N
e
tw
o
rk
(S
DN
),
”
En
g
i
n
e
e
rin
g
,
T
e
c
h
n
o
l
o
g
y
a
n
d
Ap
p
li
e
d
S
c
ien
c
e
Res
e
a
rc
h
,
v
o
l.
8
,
n
o
.
2
,
p
p
.
2
7
2
4
-
2
7
3
0
,
2
0
1
8
.
[3
2
]
A
ro
w
o
lo
M
.
O.,
A
b
d
u
lsa
lam
S
.
O.,
Isia
k
a
R.
M
.
,
G
b
o
lag
a
sd
e
K.
A
.
,
”
A
Co
m
p
a
ra
ti
v
e
A
n
a
l
y
sis
o
f
F
e
a
tu
re
S
e
lec
ti
o
n
a
n
d
F
e
a
tu
re
Ex
trac
ti
o
n
M
o
d
e
ls
f
o
r
Clas
sify
in
g
M
icro
a
rra
y
Da
t
a
se
t,
”
Co
mp
u
ti
n
g
a
n
d
In
f
o
rm
a
ti
o
n
S
y
ste
m
,
v
o
l.
2
2
,
n
o
.
2
,
p
p
.
2
9
-
3
8
,
2
0
1
8
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
Ar
o
w
o
lo
M
ich
e
a
l
O
l
a
o
l
u
,
is
a
f
a
c
u
lt
y
o
f
th
e
De
p
a
rt
m
e
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
t
L
a
n
d
m
a
rk
Un
iv
e
rsit
y
,
O
m
u
-
A
ra
n
Nig
e
ri
a
.
He
h
o
l
d
s
a
Ba
c
h
e
lo
r
De
g
re
e
f
ro
m
A
l
-
Hik
m
a
h
Un
iv
e
rsit
y
,
Ilo
rin
,
Nig
e
ria
a
n
d
a
M
a
ste
rs
De
g
re
e
f
r
o
m
K
w
a
ra
S
tate
Un
iv
e
rsit
y
,
M
a
l
e
te
Nig
e
ria,
h
e
is
p
re
se
n
tl
y
a
P
h
D
S
t
u
d
e
n
t
o
f
L
a
n
d
m
a
rk
Un
iv
e
rsity
,
O
m
u
-
A
ra
n
Nig
e
ria.
His
a
re
a
o
f
re
se
a
rc
h
in
tere
st
in
c
lu
d
e
s
M
a
c
h
in
e
L
e
a
rn
in
g
,
Bio
in
f
o
rm
a
ti
c
s,
Da
ta
m
in
in
g
,
C
y
b
e
r
S
e
c
u
rit
y
a
n
d
Co
m
p
u
ter
A
rit
h
m
e
ti
c
.
He
h
a
s
p
u
b
li
sh
e
d
w
id
e
ly
in
lo
c
a
l
a
n
d
in
tern
a
ti
o
n
a
l
re
p
u
tab
le
j
o
u
r
n
a
ls,
h
e
is
a
m
e
m
b
e
r
o
f
I
A
EN
G
,
A
P
IS
E,
S
DIW
C,
a
n
d
a
n
Ora
c
le C
e
rti
f
ied
Ex
p
e
rt.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
n
I
C
A
-
en
s
emb
le
lea
r
n
in
g
a
p
p
r
o
a
ch
es fo
r
p
r
ed
ictio
n
o
f
…
(
Mich
ea
l O
la
o
lu
A
r
o
w
o
lo
)
1569
M
a
r
io
n
O
lu
b
u
n
m
i
A
d
e
b
iy
i
,
is
a
f
a
c
u
lt
y
o
f
th
e
De
p
a
rt
m
e
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
t
Lan
d
m
a
r
k
Un
iv
e
rsit
y
,
O
m
u
-
A
r
a
n
,
Nig
e
ria.
S
h
e
h
o
l
d
s
a
B.
S
c
De
g
re
e
f
ro
m
Un
iv
e
r
sit
y
o
f
Ilo
rin
,
Il
o
ri
n
Nig
e
ria.
S
h
e
h
a
d
h
e
r
M
.
S
c
a
n
d
P
h
.
D
De
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
f
ro
m
Co
v
e
n
a
n
t
Un
iv
e
rsity
,
Nig
e
ria
r
e
sp
e
c
ti
v
e
l
y
.
He
r
r
e
se
a
r
c
h
in
tere
sts
in
c
lu
d
e
,
Bio
in
f
o
rm
a
ti
c
s
o
f
In
f
e
c
ti
o
u
s
(Af
rica
n
)
Dise
a
se
s/
P
o
p
u
latio
n
,
Org
a
n
ism
’s
In
ter
-
p
a
th
w
a
y
a
n
a
l
y
sis,
Hig
h
th
r
o
u
g
h
p
u
t
d
a
ta
a
n
a
ly
ti
c
s,
Ho
m
o
lo
g
y
m
o
d
e
ll
in
a
n
d
A
rti
f
icia
l
In
telli
g
e
n
c
e
.
S
h
e
h
a
s
p
u
b
li
sh
e
d
w
id
e
ly
in
lo
c
a
l
a
n
d
in
tern
a
ti
o
n
a
l
re
p
u
tab
le
jo
u
r
n
a
ls
S
h
e
is
a
m
e
m
b
e
r
o
f
Nig
e
rian
Co
m
p
u
ter
S
o
c
iety
(NCS),
th
e
Co
m
p
u
ter Reg
i
stra
ti
o
n
Co
u
n
c
il
o
f
Nig
e
ria (CP
N) an
d
IEE
E
m
e
m
b
e
r.
Ad
e
b
iy
i,
Ay
o
d
e
le
Ar
iy
o
,
is
a
f
a
c
u
lt
y
a
n
d
f
o
r
m
e
r
He
a
d
o
f
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
a
n
d
In
f
o
rm
a
ti
o
n
S
c
ien
c
e
s,
Co
v
e
n
a
n
t
Un
iv
e
rsit
y
,
Ota
Nig
e
ria.
He
is
c
u
rre
n
tl
y
th
e
H
e
a
d
o
f
De
p
a
rt
m
e
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
t
L
a
n
d
m
a
rk
Un
iv
e
r
sit
y
,
O
m
u
-
A
ra
n
,
N
ig
e
ria,
a
siste
r
Un
iv
e
rsit
y
to
Co
v
e
n
a
n
t
Un
iv
e
rsit
y
.
He
h
o
ld
s
a
BS
c
d
e
g
r
e
e
in
Co
m
p
u
ter
S
c
i
e
n
c
e
a
n
d
M
BA
d
e
g
re
e
f
ro
m
Un
iv
e
rsit
y
o
f
Ilo
rin
,
Ilo
ri
n
Nig
e
ria.
He
h
a
d
h
is M
S
c
a
n
d
P
h
D d
e
g
re
e
in
M
a
n
a
g
e
m
e
n
t
In
f
o
r
m
a
ti
o
n
S
y
st
e
m
(M
IS
)
f
ro
m
Co
v
e
n
a
n
t
Un
iv
e
rsity
,
Ni
g
e
ria
re
sp
e
c
ti
v
e
l
y
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
,
a
p
p
li
c
a
ti
o
n
o
f
so
f
t
c
o
m
p
u
ti
n
g
tec
h
n
i
q
u
e
s
i
n
so
lv
in
g
re
a
l
li
f
e
p
ro
b
l
e
m
s,
so
f
t
w
a
r
e
e
n
g
in
e
e
rin
g
a
n
d
in
f
o
rm
a
ti
o
n
s
y
ste
m
re
se
a
rc
h
.
He
h
a
s
su
c
c
e
ss
f
u
ll
y
m
e
n
to
re
d
a
n
d
su
p
e
rv
ise
d
se
v
e
ra
l
p
o
stg
ra
d
u
a
te
stu
d
e
n
ts
a
t
M
a
ste
rs
a
n
d
P
h
D
lev
e
l.
He
h
a
s
p
u
b
li
s
h
e
d
w
id
e
ly
in
lo
c
a
l
a
n
d
in
tern
a
ti
o
n
a
l
re
p
u
tab
l
e
jo
u
r
n
a
ls.
He
is
a
m
e
m
b
e
r
o
f
N
ig
e
rian
Co
m
p
u
ter
S
o
c
iety
(NCS),
th
e
Co
m
p
u
ter
Re
g
istratio
n
Co
u
n
c
il
o
f
Nig
e
ria (CP
N) an
d
IE
EE
m
e
m
b
e
r.
Ch
a
r
ity
Ar
e
m
u
,
is
a
f
a
c
u
lt
y
a
t
th
e
De
p
a
rt
m
e
n
t
o
f
Ag
ricu
lt
u
re
a
n
d
th
e
De
a
n
,
S
c
h
o
o
l
o
f
P
o
stg
ra
d
u
a
te
S
tu
d
ies
,
L
a
n
d
m
a
r
k
Un
iv
e
rsit
y
,
O
m
u
-
A
ra
n
,
Nig
e
ria.
Ch
a
rit
y
d
o
e
s
re
s
e
a
rc
h
in
Cro
p
En
v
iro
n
m
e
n
t
a
n
d
Im
p
ro
v
e
m
e
n
t,
P
la
n
t
Bre
e
d
i
n
g
,
Eco
l
o
g
y
,
Ev
o
lu
ti
o
n
a
ry
Bio
lo
g
y
a
n
d
G
e
n
e
ti
c
s.
S
h
e
is an
I
n
tern
a
ti
o
n
a
l
sc
h
o
lar w
it
h
sc
h
o
larly
p
u
b
li
c
a
ti
o
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.