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I
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Dr
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to
in
ef
f
icien
ci
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an
d
a
lo
w
s
u
cc
ess
r
ate
[
1
]
,
[
2
]
.
Ho
wev
er
,
th
e
d
awn
o
f
m
ac
h
in
e
lear
n
in
g
(
ML
)
an
d
ar
tific
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in
tellig
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ce
(
AI
)
o
f
f
er
s
h
o
p
e,
h
o
ld
in
g
im
m
en
s
e
p
o
ten
tial
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tr
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m
lin
e
th
e
d
r
u
g
d
is
co
v
er
y
p
r
o
ce
s
s
[
3
]
–
[
5
]
.
T
h
is
s
tu
d
y
d
elv
es
in
to
th
is
ex
citin
g
f
r
o
n
tier
b
y
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m
p
a
r
in
g
class
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ML
tech
n
iq
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es with
th
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cu
ttin
g
-
e
d
g
e
r
ea
lm
o
f
a
u
to
m
ated
m
ac
h
in
e
lea
r
n
in
g
(
Au
to
ML
)
[
6
]
,
[
7
]
.
T
h
e
f
o
c
u
s
h
o
n
e
d
in
o
n
t
h
e
ep
i
d
er
m
al
g
r
o
wth
f
ac
t
o
r
r
ec
e
p
to
r
(
E
GFR
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,
a
cr
itical
p
r
o
tein
im
p
licated
in
m
an
y
ca
n
ce
r
s
[
8
]
–
[
1
0
]
.
T
o
u
n
lo
ck
v
alu
a
b
le
in
s
ig
h
ts
,
we
lev
er
ag
e
th
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im
m
en
s
e
co
m
p
u
tatio
n
al
p
o
wer
o
f
Ap
ac
h
e
Sp
ar
k
[
1
1
]
,
[
1
2
]
.
T
h
is
allo
ws
u
s
to
ef
f
icien
tly
an
aly
ze
v
ast
an
d
d
iv
e
r
s
e
d
atasets
en
co
m
p
ass
in
g
b
io
lo
g
ical,
ch
e
m
ical,
an
d
g
en
o
m
ic
d
ata,
all
in
tr
icate
ly
lin
k
e
d
to
E
GFR
[
1
3
]
,
[
1
4
]
.
C
las
s
ical
ML
ap
p
r
o
ac
h
es
Xu
et
a
l
.
[
7
]
u
tili
ze
d
class
ical
ML
alg
o
r
ith
m
s
s
u
ch
as
lin
ea
r
r
eg
r
ess
io
n
(
L
R
)
an
d
r
an
d
o
m
f
o
r
est
(
R
F)
f
o
r
p
r
ed
ictin
g
d
r
u
g
b
io
ac
tiv
i
ty
ag
ain
s
t
ca
n
ce
r
tar
g
ets
[
1
5
]
–
[
1
7
]
.
T
h
ei
r
s
tu
d
y
d
em
o
n
s
tr
ated
th
e
e
f
f
ec
tiv
en
e
s
s
o
f
ML
in
d
r
u
g
d
is
co
v
er
y
task
s
[
1
8
]
–
[
2
0
]
.
T
h
e
au
th
o
r
s
p
r
o
p
o
s
ed
a
d
ee
p
lear
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in
g
-
b
ased
b
io
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tiv
ity
p
r
e
d
ictio
n
s
y
s
tem
,
ac
h
ie
v
in
g
s
u
p
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r
m
an
ce
co
m
p
ar
ed
to
tr
ad
itio
n
al
M
L
m
eth
o
d
s
[
1
2
]
,
[
1
6
]
,
[
2
1
]
.
Au
to
ML
-
b
ased
ap
p
r
o
ac
h
es
h
av
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b
ee
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a
p
p
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to
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r
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is
co
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task
s
,
n
o
tab
l
y
th
r
o
u
g
h
Au
t
o
ML
-
Z
er
o
[
1
6
]
,
[
2
2
]
,
[
2
3
]
,
wh
ich
d
em
o
n
s
tr
ated
th
at
Au
to
ML
f
r
am
ewo
r
k
s
ca
n
r
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al
m
an
u
all
y
co
n
s
tr
u
cted
p
i
p
elin
es
an
d
o
f
f
er
a
m
o
r
e
ef
f
icien
t
a
p
p
r
o
ac
h
to
m
o
d
el
d
ev
elo
p
m
en
t
[
2
4
]
–
[
2
6
]
.
Ad
d
itio
n
ally
,
Evaluation Warning : The document was created with Spire.PDF for Python.
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J
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20
26
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s
[
1
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[
1
6
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[
2
6
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2.
M
E
T
H
O
D
2
.
1
.
Co
m
pu
t
a
t
io
na
l
a
rc
hite
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s
pa
rk
env
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t
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,
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d
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o
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s
t
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Fi
g
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r
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1
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th
e
o
b
jec
ti
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f
t
h
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s
s
tu
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y
is
t
o
b
u
il
d
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p
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is
ti
n
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ese
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h
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y
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o
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p
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cl
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M
L
tec
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n
i
q
u
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wit
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A
u
t
o
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L
a
p
p
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o
ac
h
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u
s
i
n
g
Ap
ac
h
e
S
p
ar
k
[
2
6
]
,
[
2
7
]
.
T
h
e
p
e
r
f
o
r
m
a
n
c
e
an
d
s
c
ala
b
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o
f
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a
p
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ch
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n
p
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d
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ti
n
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b
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v
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ty
f
o
r
t
h
e
E
GFR
ta
r
g
et
ar
e
ev
al
u
a
te
d
[
2
7
]
.
T
wo
m
a
in
m
e
th
o
d
o
l
o
g
ies
a
r
e
p
r
o
p
o
s
e
d
:
a
class
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al
M
L
p
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in
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a
n
d
an
Au
to
ML
ap
p
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Ap
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wh
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r
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[
2
7
]
,
[
2
8
]
.
Fig
u
r
e
1
.
Ov
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v
iew
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th
e
d
r
u
g
d
is
co
v
er
y
p
r
o
ce
s
s
2
.
2
.
E
G
F
R
bio
a
ct
i
v
it
y
da
t
a
a
cquis
it
io
n
T
h
e
class
ical
ML
p
ip
elin
e
in
v
o
lv
es
a
s
tep
-
by
-
s
tep
p
r
o
ce
s
s
s
tar
tin
g
with
th
e
ac
q
u
is
itio
n
o
f
d
ata
f
r
o
m
th
e
C
h
E
MBL
d
atab
ase
p
er
tin
en
t
to
th
e
E
GFR
tar
g
et.
T
h
is
is
f
o
llo
wed
b
y
a
m
eticu
lo
u
s
d
ata
p
r
ep
r
o
ce
s
s
in
g
p
h
ase
en
co
m
p
ass
in
g
clea
n
in
g
,
p
r
ep
r
o
ce
s
s
in
g
,
an
d
tr
a
n
s
f
o
r
m
atio
n
o
f
th
e
ac
q
u
ir
e
d
d
a
ta
to
p
r
ep
ar
e
it
f
o
r
an
aly
s
is
[
2
8
]
.
Su
b
s
eq
u
e
n
tly
,
f
ea
tu
r
e
en
g
in
ee
r
in
g
ex
tr
ac
ts
r
elev
an
t
f
ea
tu
r
es
s
u
ch
as
m
o
lecu
lar
d
escr
ip
to
r
s
a
n
d
b
io
lo
g
ical
p
r
o
p
er
ties
f
r
o
m
th
e
p
r
ep
r
o
ce
s
s
ed
d
ata
[
2
9
]
.
2
.
3
.
Da
t
a
prepro
ce
s
s
ing
a
nd
f
ea
t
ure
eng
ineering
Data
p
r
ep
r
o
ce
s
s
in
g
:
f
o
r
ca
lc
u
latin
g
m
o
lecu
la
r
d
escr
ip
to
r
s
,
o
n
ly
th
e
co
lu
m
n
s
m
o
lecu
le
C
h
em
b
lI
d
,
ca
n
o
n
icalSm
iles
,
an
d
s
tan
d
ar
d
v
alu
e
ar
e
r
etain
ed
,
as
th
ey
ar
e
ess
en
tial
f
o
r
m
o
lecu
le
i
d
en
ti
f
icatio
n
,
m
o
lecu
lar
d
escr
ip
to
r
s
ca
lcu
latio
n
,
an
d
r
ep
r
esen
tin
g
th
e
d
ep
e
n
d
en
t
v
ar
iab
le
(
Y
)
,
r
esp
ec
tiv
ely
.
T
h
is
in
itial
s
elec
tio
n
en
s
u
r
es
th
at
th
e
d
ataset
co
n
tain
s
o
n
ly
th
e
co
r
e
ch
em
ica
l
s
tr
u
ctu
r
e
an
d
th
e
ex
p
er
im
en
tally
d
eter
m
in
e
d
b
io
ac
tiv
ity
r
eq
u
ir
ed
f
o
r
q
u
an
t
itativ
e
an
aly
s
is
.
E
m
p
ty
r
o
ws,
wh
ich
d
o
n
o
t
co
r
r
esp
o
n
d
to
t
h
e
ex
p
ec
ted
b
in
ar
y
v
alu
es in
o
u
r
d
ataset
o
f
m
o
lec
u
lar
f
in
g
e
r
p
r
in
ts
,
ar
e
r
em
o
v
ed
an
d
in
ter
p
r
eted
as o
u
tlier
s
[
2
9
]
.
Data
tr
an
s
f
o
r
m
atio
n
:
th
e
p
r
im
ar
y
tr
an
s
f
o
r
m
atio
n
in
v
o
l
v
es
co
n
v
er
tin
g
th
e
tar
g
et
v
ar
iab
le,
PIC5
0
,
to
th
e
n
eg
ativ
e
lo
g
a
r
ith
m
ic
s
ca
le,
wh
ich
is
s
p
ec
if
ically
[
3
0
]
.
T
h
e
PIC5
0
s
ca
le
is
s
t
an
d
ar
d
p
r
ac
tice
in
ch
em
in
f
o
r
m
atics,
as
it
n
o
r
m
alize
s
th
e
co
n
ce
n
tr
atio
n
v
alu
es
(
I
C
5
0
)
an
d
s
im
p
lifie
s
i
n
ter
p
r
etatio
n
.
T
h
is
co
n
v
er
s
io
n
h
elp
s
ac
h
iev
e
a
m
o
r
e
u
n
if
o
r
m
(
h
o
m
o
g
en
e
o
u
s
)
d
is
tr
ib
u
tio
n
o
f
v
al
u
es,
f
ac
ilit
atin
g
c
o
m
p
ar
is
o
n
s
an
d
f
u
r
th
er
an
al
y
s
es
[
3
0
]
.
Featu
r
e
s
elec
tio
n
:
th
is
s
tep
was
u
n
d
er
tak
en
to
ch
o
o
s
e
th
e
m
o
s
t
r
elev
an
t
an
d
in
f
o
r
m
ativ
e
v
ar
iab
les,
aim
in
g
to
o
p
tim
ize
alg
o
r
ith
m
p
e
r
f
o
r
m
an
ce
wh
ile
r
ed
u
cin
g
m
o
d
el
c
o
m
p
lex
ity
.
A
lo
o
p
with
f
C
lass
if
was
im
p
lem
en
ted
f
o
r
f
ea
tu
r
e
s
elec
tio
n
,
wh
ich
r
elies
o
n
th
e
an
aly
s
is
o
f
v
ar
ian
ce
test
to
ass
es
s
f
ea
tu
r
e
r
elev
an
ce
with
r
esp
ec
t to
t
h
e
t
ar
g
et
v
ar
iab
le
i
n
a
r
e
g
r
ess
io
n
c
o
n
tex
t
[
3
1
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
S
p
a
r
k
-
p
o
w
ered
b
io
a
ctivity
p
r
ed
ictio
n
:
a
c
o
mp
a
r
is
o
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f m
a
ch
in
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lea
r
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g
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fo
)
2425
2
.
4
.
Da
t
a
s
pli
t
t
ing
(
t
ra
ini
ng
a
nd
t
est
s
et
s
)
B
y
iter
ativ
ely
ad
ju
s
tin
g
th
e
p
a
r
am
eter
,
th
e
o
p
tim
al
n
u
m
b
er
o
f
f
ea
tu
r
es
was
s
ea
r
ch
ed
to
m
in
im
ize
th
e
m
ea
n
s
q
u
a
r
ed
e
r
r
o
r
o
n
a
test
s
et.
T
h
e
R
F
m
o
d
el
id
en
tifie
d
th
e
to
p
5
m
o
s
t
im
p
o
r
ta
n
t
f
ea
tu
r
es,
as
s
h
o
wn
i
n
Fig
u
r
e
2
Pu
b
ch
em
FP
3
7
8
,
Pu
b
ch
em
FP
6
0
1
,
Pu
b
ch
em
FP
4
4
7
,
Pu
b
ch
e
m
FP
3
7
3
,
an
d
Pu
b
ch
em
FP
1
8
0
.
T
h
e
p
r
o
ject
is
a
s
u
p
er
v
is
ed
lear
n
i
n
g
task
wh
e
r
e
th
e
d
ata
co
llectio
n
was
d
iv
i
d
ed
i
n
to
two
p
a
r
ts
:
th
e
tr
ain
in
g
s
am
p
le,
wh
ich
r
ep
r
esen
ts
th
e
m
ain
p
o
r
tio
n
o
f
th
e
d
ataset
wh
er
e
alg
o
r
ith
m
s
lear
n
,
c
o
m
p
r
is
es
8
0
%
o
f
th
e
to
tal
d
ata
[
3
1
]
.
T
h
e
r
em
ai
n
in
g
2
0
%
c
o
n
s
titu
tes
th
e
test
s
am
p
le,
wh
ich
is
ex
clu
s
iv
ely
u
s
ed
t
o
ev
al
u
at
e
th
e
f
in
al
m
o
d
el'
s
p
er
f
o
r
m
an
ce
o
n
u
n
s
ee
n
d
ata
b
y
co
m
p
a
r
in
g
its
p
r
ed
ictio
n
s
ag
ain
s
t
th
e
tr
u
e
tar
g
et
v
ar
iab
l
e
v
alu
es
[
3
1
]
.
T
h
is
p
ar
titi
o
n
in
g
m
eth
o
d
en
s
u
r
es
a
r
eliab
le
an
d
o
b
jectiv
e
ass
es
s
m
en
t
o
f
m
o
d
el
ac
cu
r
ac
y
an
d
r
o
b
u
s
tn
ess
b
ef
o
r
e
d
ep
lo
y
m
e
n
t in
a
r
ea
l
-
wo
r
ld
s
c
en
ar
io
.
Fig
u
r
e
2
.
T
o
p
5
f
ea
tu
r
e
im
p
o
r
t
an
ce
s
co
r
es f
r
o
m
R
F
2
.
5
.
Cla
s
s
ica
l
ma
chine le
a
rning
m
o
del im
plem
ent
a
t
io
n
T
h
e
m
o
d
el
s
elec
tio
n
p
h
ase
th
en
en
s
u
es,
wh
er
e
class
ical
M
L
m
o
d
els
lik
e
L
R
an
d
R
F
ar
e
ca
r
ef
u
lly
ch
o
s
en
[
3
1
]
,
[
3
2
]
.
T
h
ese
s
ele
cted
m
o
d
els
ar
e
tr
ain
ed
o
n
th
e
p
r
ep
r
o
ce
s
s
ed
d
ata,
f
o
llo
we
d
b
y
an
ev
alu
atio
n
p
h
ase
to
ass
ess
th
eir
p
er
f
o
r
m
a
n
ce
u
s
in
g
ap
p
r
o
p
r
iate
m
etr
ics
[
3
2
]
.
T
h
e
n
ex
t
s
tep
th
e
n
in
v
o
lv
es
f
in
e
-
tu
n
in
g
th
e
h
y
p
er
p
ar
am
eter
s
o
f
th
e
b
est
-
p
er
f
o
r
m
in
g
m
o
d
els to
f
u
r
th
er
o
p
tim
ize
th
e
r
esu
lts
[
3
2
]
.
2
.
6
.
Aut
o
m
a
t
ed
m
a
chine le
a
rning
im
plem
ent
a
t
io
n
T
h
e
Au
to
ML
ap
p
r
o
ac
h
was
im
p
lem
en
ted
b
y
lev
er
a
g
in
g
th
e
d
is
tr
ib
u
ted
p
r
o
ce
s
s
in
g
in
f
r
as
tr
u
ctu
r
e
o
f
Ap
ac
h
e
Sp
ar
k
[
3
3
]
.
Sp
ec
if
ically
,
th
is
s
tu
d
y
u
tili
ze
s
Ap
ac
h
e
Sp
ar
k
ML
lib
,
Sp
ar
k
'
s
ML
lib
r
ar
y
,
to
s
u
p
p
o
r
t
th
e
au
to
m
atio
n
s
tep
s
f
o
r
u
p
s
tr
ea
m
task
s
,
in
clu
d
in
g
p
r
ep
r
o
ce
s
s
in
g
an
d
f
ea
tu
r
e
en
g
in
ee
r
in
g
[
3
3
]
.
T
o
th
is
e
n
d
,
a
cu
s
to
m
-
b
u
ilt Au
to
ML
p
ip
elin
e
was d
ev
elo
p
ed
to
m
an
ag
e
tas
k
o
r
ch
estra
tio
n
a
n
d
h
y
p
e
r
p
ar
a
m
eter
o
p
tim
izatio
n
,
ac
tin
g
as
an
ad
h
o
c
m
ec
h
a
n
is
m
f
o
r
th
e
iter
ativ
e
e
v
alu
atio
n
o
f
m
u
ltip
le
alg
o
r
ith
m
s
[
3
3
]
.
T
h
is
p
r
o
ce
s
s
co
m
p
ar
ed
LR
an
d
RF
,
u
ltima
tely
s
elec
tin
g
th
e
g
r
ad
ien
t
-
b
o
o
s
ted
tr
ee
r
eg
r
ess
o
r
(
GB
T
r
eg
r
ess
o
r
)
m
o
d
el
f
r
o
m
ML
lib
as th
e
m
o
s
t p
er
f
o
r
m
an
t
f
o
r
E
GFR
b
io
ac
tiv
ity
p
r
e
d
ictio
n
[
3
3
]
,
[
3
4
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Giv
en
th
e
wid
e
r
an
g
e
o
f
p
o
s
s
ib
ilit
ies,
th
e
im
p
lem
en
tatio
n
p
a
r
t
o
f
class
ical
m
o
d
els
is
f
ir
s
t
f
o
cu
s
ed
o
n
u
s
in
g
f
o
u
r
p
r
e
d
ictiv
e
alg
o
r
ith
m
s
,
ea
ch
with
its
ad
v
a
n
tag
es:
L
R
,
RF
an
d
GB
T
r
eg
r
ess
o
r
[
3
5
]
.
T
h
e
p
r
o
ject
u
tili
ze
d
a
s
u
p
er
v
is
ed
lear
n
in
g
ap
p
r
o
ac
h
,
with
th
e
co
llected
d
ata
d
iv
id
ed
in
t
o
two
d
is
tin
ct
p
ar
ts
[
3
5
]
.
T
h
is
d
iv
is
io
n
is
a
f
u
n
d
am
en
tal
s
tep
in
ML
to
p
r
ev
en
t
m
o
d
el
o
v
e
r
f
itti
n
g
a
n
d
en
s
u
r
e
th
e
p
r
ed
ictiv
e
s
y
s
tem
g
en
er
alize
s
well
to
n
ew,
u
n
s
ee
n
d
ata.
C
o
n
s
eq
u
en
tly
,
th
e
en
tire
d
ataset
was
s
y
s
tem
atica
ll
y
p
ar
titi
o
n
ed
in
t
o
a
tr
ain
in
g
s
et
f
o
r
m
o
d
el
lear
n
in
g
an
d
a
s
ep
ar
ate
test
s
et
f
o
r
f
in
a
l p
er
f
o
r
m
an
ce
ev
al
u
atio
n
[
3
6
]
.
3
.
1
.
Co
m
pa
riso
n o
f
cl
a
s
s
ica
l
m
a
chine le
a
rning
a
nd
a
uto
m
a
t
ed
m
a
chine le
a
rning
re
s
ults
Fig
u
r
es
3
an
d
4
co
m
p
a
r
e
th
e
p
er
f
o
r
m
an
ce
o
f
class
ical
M
L
an
d
A
u
to
ML
ap
p
r
o
ac
h
es
in
ter
m
s
o
f
p
r
ed
ictio
n
a
cc
u
r
ac
y
,
s
ca
lab
ilit
y
,
an
d
ef
f
icien
c
y
.
W
h
en
co
m
p
ar
in
g
t
h
e
Au
to
ML
m
o
d
el
to
class
ical
m
o
d
els,
d
is
tin
ct
ad
v
an
tag
es
ar
e
o
b
s
er
v
ed
[
3
6
]
.
T
h
e
Au
t
o
ML
m
o
d
el
d
em
o
n
s
tr
ates
s
u
p
er
io
r
ac
cu
r
a
cy
wh
en
co
m
p
ar
ed
with
th
e
ac
tu
al
v
alu
es
in
o
u
r
d
ataset.
Ad
d
itio
n
ally
,
it
ex
h
i
b
its
f
ewe
r
o
u
tlier
s
,
in
d
icatin
g
a
h
ig
h
er
lev
el
o
f
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2426
p
r
ec
is
io
n
an
d
r
eliab
ilit
y
in
its
p
r
ed
ictio
n
s
[
3
6
]
.
T
h
ese
ch
ar
ac
ter
is
tics
s
u
g
g
est
th
at
th
e
Au
to
ML
ap
p
r
o
ac
h
is
ca
p
ab
le
o
f
p
r
o
v
id
in
g
m
o
r
e
ac
c
u
r
ate
an
d
t
r
u
s
two
r
th
y
r
esu
lts
[
3
6
]
,
[
3
7
]
.
Fig
u
r
e
3.
Acc
u
r
ac
y
of
p
r
ed
ict
io
n
s
:
class
ica
l
ML
mode
ls
Fig
u
r
e
4.
Acc
u
r
ac
y
of
p
r
e
d
ic
tio
n
s
:
au
to
ML
m
o
d
els
C
o
n
v
er
s
ely
,
th
e
class
ical
m
o
d
els
ex
h
ib
it
ce
r
tain
lim
itatio
n
s
[
3
7
]
.
T
h
ey
d
is
p
lay
lo
wer
lev
els
o
f
ac
cu
r
ac
y
,
as
ev
id
e
n
ce
d
b
y
th
e
g
r
ea
ter
d
is
p
er
s
io
n
o
f
p
o
in
ts
,
o
f
ten
d
ev
iatin
g
s
ig
n
if
ican
tly
f
r
o
m
th
e
i
d
en
tity
lin
e.
Mo
r
eo
v
er
,
th
ese
m
o
d
els
ten
d
to
h
a
v
e
m
o
r
e
o
u
tlier
s
,
im
p
l
y
in
g
a
h
eig
h
te
n
ed
r
is
k
o
f
s
u
b
s
tan
tial
er
r
o
r
s
in
Evaluation Warning : The document was created with Spire.PDF for Python.
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tell
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3
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S
p
a
r
k
-
p
o
w
ered
b
io
a
ctivity
p
r
ed
ictio
n
:
a
c
o
mp
a
r
is
o
n
o
f m
a
ch
in
e
lea
r
n
in
g
…
(
N
a
z
if Tch
a
g
a
fo
)
2427
p
r
ed
ictio
n
[
3
7
]
.
I
n
s
u
m
m
ar
y
,
th
e
co
m
p
ar
is
o
n
b
etwe
en
Au
to
ML
an
d
class
ical
m
o
d
els
r
ev
ea
ls
th
e
f
o
r
m
er
’
s
s
u
p
er
io
r
ity
in
ter
m
s
o
f
p
r
ed
ic
tio
n
ac
cu
r
ac
y
.
T
h
e
Au
to
ML
ap
p
r
o
ac
h
n
o
t
o
n
ly
ac
h
ie
v
ed
h
ig
h
er
p
r
ec
is
io
n
b
u
t
also
d
em
o
n
s
tr
ated
g
r
ea
ter
r
e
liab
ilit
y
in
its
p
r
ed
ictio
n
s
,
m
ak
in
g
it
a
s
u
p
er
io
r
ch
o
ice
f
o
r
th
is
b
io
ac
tiv
ity
m
o
d
elin
g
task
[
3
7
]
.
T
h
e
co
n
f
u
s
io
n
m
atr
ix
allo
ws
u
s
to
v
is
u
alize
th
e
p
r
e
d
ictio
n
s
o
f
th
e
m
o
d
el
an
d
co
m
p
ar
e
th
em
to
th
e
ac
tu
al
v
alu
es.
I
t
p
r
o
v
i
d
es
a
d
etailed
u
n
d
e
r
s
tan
d
in
g
o
f
m
o
d
el
p
er
f
o
r
m
an
ce
,
id
e
n
tify
in
g
co
r
r
ec
tly
an
d
in
co
r
r
ec
tly
p
r
ed
icted
ca
s
es.
Sp
ec
if
ically
in
th
e
co
n
tex
t
o
f
E
GFR
b
io
ac
tiv
ity
p
r
ed
ictio
n
,
th
e
m
atr
i
x
h
elp
s
q
u
an
tify
c
r
u
cial
m
etr
ics lik
e
p
r
ec
is
io
n
an
d
r
ec
all
f
o
r
class
if
y
in
g
ac
tiv
e
v
er
s
u
s
in
ac
tiv
e
co
m
p
o
u
n
d
s
[
3
7
]
.
T
h
e
an
aly
s
is
o
f
T
ab
le
1
r
ev
ea
l
s
th
e
p
er
f
o
r
m
a
n
ce
o
f
v
a
r
io
u
s
m
o
d
els
u
s
ed
t
o
p
r
ed
ict
th
e
b
io
ac
tiv
ity
o
f
th
e
E
GFR
tar
g
et.
E
v
alu
ated
m
etr
ics in
clu
d
e
r
o
o
t m
e
an
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
,
m
ea
n
ab
s
o
lu
t
e
er
r
o
r
(
MA
E
)
,
an
d
th
e
co
ef
f
icien
t
o
f
d
eter
m
i
n
atio
n
(
R
²)
.
Ov
er
all,
th
e
Au
to
ML
ap
p
r
o
ac
h
o
u
t
p
er
f
o
r
m
s
in
d
iv
i
d
u
al
m
o
d
els
s
u
ch
as
R
F,
L
R
,
G
B
T
r
eg
r
ess
o
r
,
an
d
L
R
in
ter
m
s
o
f
p
r
ed
ictiv
e
ac
cu
r
ac
y
[
3
7
]
,
[
3
8
]
.
Sp
e
cif
ically
,
Au
to
ML
d
em
o
n
s
tr
ates
lo
wer
R
MSE
an
d
MA
E
v
alu
es,
in
d
icatin
g
b
etter
alig
n
m
en
t
b
etwe
en
p
r
e
d
ictio
n
s
an
d
ac
tu
al
v
alu
es.
Ad
d
itio
n
ally
,
Au
to
M
L
ac
h
iev
es
a
h
ig
h
er
R
²,
s
u
g
g
esti
n
g
a
b
etter
ex
p
lan
atio
n
o
f
d
ata
v
a
r
ian
ce
co
m
p
ar
ed
to
o
th
e
r
m
o
d
els
[
3
8
]
.
T
h
ese
r
esu
lts
u
n
d
er
s
co
r
e
th
e
ef
f
ec
tiv
e
n
ess
o
f
au
to
m
atio
n
in
m
o
d
el
s
elec
tio
n
a
n
d
co
n
f
ig
u
r
atio
n
,
lead
in
g
to
im
p
r
o
v
ed
p
e
r
f
o
r
m
an
ce
in
p
r
ed
ictin
g
E
GFR
tar
g
et
b
io
ac
tiv
ity
.
Ad
d
itio
n
al
ly
,
b
o
th
ap
p
r
o
ac
h
es
ex
h
ib
it
s
im
ilar
tr
ain
in
g
tim
e
s
,
with
th
e
Au
to
ML
ap
p
r
o
a
ch
d
em
o
n
s
tr
atin
g
s
lig
h
tly
f
a
s
ter
tr
ain
in
g
tim
es
co
m
p
ar
ed
to
th
e
class
ical
ML
ap
p
r
o
ac
h
[
3
8
]
.
T
h
is
s
u
g
g
ests
th
at
lev
er
ag
in
g
Ap
ac
h
e
Sp
a
r
k
f
o
r
Au
to
ML
task
s
ca
n
lead
t
o
im
p
r
o
v
e
d
ef
f
icien
c
y
in
m
o
d
el
tr
ain
in
g
.
As
we
ca
n
s
ee
in
t
h
e
co
m
p
ar
is
o
n
o
f
th
e
p
er
f
o
r
m
an
ce
g
r
a
p
h
in
Fig
u
r
e
5
.
T
ab
le
1
.
Mo
d
els’
m
etr
ics
M
e
t
r
i
c
s
A
u
t
o
M
L
RF
LR
G
B
TR
C
a
t
B
o
o
s
t
R
M
S
E
0
.
9
8
1
.
1
8
1
.
1
4
1
.
1
3
1
.
2
2
M
A
E
0
.
7
0
.
9
3
0
.
8
8
0
.
8
5
0
.
8
6
R
2
0
.
5
5
0
.
3
3
0
.
3
7
0
.
3
9
0
.
4
4
Fig
u
r
e
5
.
C
o
m
p
a
r
is
o
n
o
f
p
er
f
o
r
m
an
ce
b
etwe
en
class
ical
an
d
A
u
to
ML
m
o
d
els b
ased
o
n
R
-
s
q
u
ar
ed
3
.
2
.
E
rr
o
r
a
na
ly
s
is
a
nd
lim
it
a
t
io
ns
Desp
ite
d
em
o
n
s
tr
atin
g
s
u
p
e
r
i
o
r
ity
o
v
er
class
ical
m
o
d
els,
A
u
to
ML
ap
p
r
o
ac
h
y
ield
ed
R²
o
f
0
.
5
5
[
3
8
]
.
T
h
is
v
alu
e
is
a
m
o
d
est
p
er
f
o
r
m
an
ce
f
o
r
a
cr
itical
ap
p
licat
io
n
lik
e
d
r
u
g
d
is
co
v
er
y
,
as
it
in
d
icate
s
th
at
th
e
m
o
d
el
ex
p
lain
s
o
n
ly
ab
o
u
t
o
f
th
e
b
io
ac
tiv
ity
v
ar
ian
c
e
[
1
7
]
.
Fu
r
th
er
m
o
r
e,
th
e
R
MSE
o
f
s
ig
n
als
a
non
-
n
eg
lig
ib
le
m
ar
g
in
o
f
er
r
o
r
in
th
e
p
r
ed
ictio
n
s
,
h
i
g
h
lig
h
tin
g
th
e
n
ee
d
f
o
r
f
u
r
th
er
r
e
f
in
em
en
t
to
en
s
u
r
e
h
ig
h
er
r
eliab
ilit
y
[
1
7
]
.
T
o
p
u
r
s
u
e
h
ig
h
er
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
,
o
u
r
n
e
x
t
r
esear
ch
p
h
ase
will
in
v
o
lv
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
26
:
2
4
2
3
-
2
4
3
0
2428
ad
v
an
ce
d
d
ee
p
lear
n
in
g
ar
ch
it
ec
tu
r
es,
s
p
ec
if
ically
C
NN
an
d
ANN,
alo
n
g
s
id
e
th
e
in
teg
r
ati
o
n
o
f
a
s
u
b
s
tan
tial
in
cr
ea
s
e
in
d
ata
v
o
lu
m
e
[
1
7
]
,
[
3
8
]
.
4.
CO
NCLU
SI
O
N
Acc
u
r
ately
p
r
ed
ictin
g
th
e
b
i
o
a
ctiv
ity
o
f
p
o
ten
tial
d
r
u
g
ca
n
d
i
d
ates
is
a
cr
u
cial
s
tep
in
th
e
n
o
to
r
io
u
s
ly
s
lo
w
an
d
e
x
p
en
s
iv
e
d
r
u
g
d
is
co
v
er
y
p
r
o
ce
s
s
.
T
h
is
s
tu
d
y
in
v
e
s
tig
ates
th
e
p
o
ten
tial
o
f
Ap
ac
h
e
Sp
ar
k
to
lev
e
r
ag
e
ML
f
o
r
m
o
r
e
ef
f
icien
t
d
r
u
g
d
is
co
v
er
y
b
y
co
n
d
u
ctin
g
a
c
o
m
p
ar
ativ
e
an
aly
s
is
o
f
class
ical
ML
tech
n
iq
u
es
an
d
Au
to
ML
.
W
e
f
o
c
u
s
o
n
th
e
E
G
FR
,
a
p
r
o
tein
k
n
o
w
n
to
p
lay
a
ce
n
tr
al
r
o
le
in
th
e
d
ev
el
o
p
m
en
t
o
f
m
a
n
y
ca
n
ce
r
s
.
B
y
ac
cu
r
ately
p
r
ed
ictin
g
th
e
b
io
ac
tiv
ity
o
f
p
o
ten
tial
d
r
u
g
s
tar
g
etin
g
E
GFR
,
r
esear
ch
er
s
ca
n
p
r
io
r
itize
p
r
o
m
is
in
g
ca
n
d
id
ates
an
d
ex
p
ed
ite
th
e
d
r
u
g
d
is
co
v
er
y
p
r
o
ce
s
s
.
T
h
is
s
tu
d
y
co
n
d
u
cts
a
co
m
p
ar
ativ
e
an
aly
s
is
o
f
two
k
ey
ML
ap
p
r
o
ac
h
es:
class
ical
ML
tech
n
iq
u
es
an
d
Au
to
ML
.
T
h
e
c
o
r
e
o
b
jectiv
e
o
f
t
h
is
co
m
p
a
r
is
o
n
was
to
d
eter
m
in
e
wh
ich
a
p
p
r
o
ac
h
o
f
f
er
s
th
e
m
o
s
t
ef
f
icien
t,
s
ca
lab
le,
an
d
ac
cu
r
ate
p
ip
elin
e
f
o
r
th
is
cr
itical
tar
g
et
in
d
r
u
g
d
is
co
v
er
y
.
C
lass
ical
ML
m
eth
o
d
s
r
eq
u
ir
e
s
ig
n
if
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.
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