Experiments of 5-fold Cross-Validation on five kingdoms using 1-Nearest-Neighbor

*For ontology and other abbreviations, see here

Table 1: Confusion matrix for Animal group

Prd ⇒
Obs ⇓
nuc  mem  mit  cyt  ext  gol  pex  end  lys  N.P.  rowSUM  recall 
nuc 2512
5
0
13
2
0
0
1
0
313
2846 .883
mem 6
4630
2
12
43
6
0
4
1
116
4820 .961
mit
0
1
1134
7
1
0
0
0
0
55
1198 .947
cyt
15
17
2
1630
1
0
1
1
0
178
1845
.883
ext
0
50
0
9
3409
0
0
2
3
470
3943 .864
gol
1
4
0
0
0
145
0
3
1
13
167 .868
pex
0
0
0
2
0
0
100
0
0
1
103 .971
end
1
3
0
1
0
2
0
431
0
19
457 .943
lys
0
0
0
1
0
0
0
0
161
8
170
.947
colSUM 2535
4710
1138
1675
3456
153
101
442
166
1173
15549 O.R. = .910
precision .991
.983
.996
.973
.986
.948
.990
.975
.969
S.C. = .924
  O.P. = .984
specificity
.998
.992
.999
.997
.996
.999
.999
.999
.999


O.S. = .995

Table 2: Confusion matrix for Plant group

Prd ⇒
Obs ⇓
nuc  mit  cyt  ext  gol  chl  pex  end  vac  mem  N.P.  rowSUM   recall 
nuc 160
0
1
0
0
0
0
0
0
1
6
168 .952
mit 2
256
2
0
0
8
2
0
0
0
37
307 .833
cyt 0
0
409
0
0
6
0
0
0
0
32
447 .915
ext 0
0
1
66
0
0
0
0
0
1
59
127 .519
gol 0
0
0
0
24
0
0
0
0
3
8
35 .686
chl 1
11
14
0
0
1797
0
1
0
1
74
1899 .946
pex 0
0
0
0
0
0
28
0
0
0
1
29 .955
end 0
0
0
0
0
1
0
56
0
2
5
64 .875
vac 0
0
0
0
0
0
0
0
67
2
13
82 .817
mem 0
0
2
0
0
1
0
2
2
101
27
135 .748
colSUM 163
257
429
66
24
1813
30
59
69
111
262
3293 O.R. = .900
precision .982
.959
.953
1.00
1.00
.991
.933
.949
.971
.909
S.C. = .920
  O.P. = .978
specificity
.999
.996
.993
1.00
1.00
.989
.999
.999
.999
.997


O.S. = .998

Table: Confusion matrix for Fungi Group
 
Prd ⇒
Obs ⇓
nuc  mit  cyt  ext  gol  pex  end  mem  vac  N.P.  rowSUM   recall 
nuc 440
2
1
0
1
0
0
2
0
175
621 .709
mit 2
294
17
0
0
1
1
4
0
87
406 .724
cyt 2
11
290
0
0
1
1
2
0
88
395 .734
ext 0
0
3
101
1
0
1
3
0
62
171 .591
gol 0
0
0
0
31
0
0
7
0
14
52 .596
pex 0
2
2
0
0
54
0
0
0
6
64 .834
end 1
0
0
2
2
0
46
2
1
10
64 .719
mem 0
0
2
0
2
0
3
264
2
29
302 .875
vac 0
0
2
1
0
0
0
1
1
14
19 .053
colSUM 445
309
317
104
37
56
52
285
4
485
2094  O.R. = .726
precision .989
.951
.915
.971
.839
.964
.886
.926
.250
S.C = .768
  O.P. = .945
specificity
.997
.991
.984
.998
.997
.999
.997
.988
.998


O.S. = .992

Table: Confusion matrix for Gram-positive Bacteria group

Prd ⇒
Obs ⇓
cyt  wal  ext  mem  N.P.  rowSUM  recall 
cyt 836
0
0
1
93
930 .899
wal
0
15
0
0
4
19
.789
ext 1
3
175
5
68
252 .694
mem 3
1
5
225
106
340 .662
colSUM 840
19
180
231
271
1541  O.R. = .812
precision .995
.789
.972
.974
S.C. = .824
  O.P. = .985
specificity
.997
.997
.996
.995


O.S. = .994

Table: Confusion matrix for Gram-negative Bacteria group
 
Prd ⇒
Obs ⇓
cyt  ext  per  inn  wal  out  N.P.  rowSUM   recall 
cyt 1692
0
4
1
0
0
164
1861 .909
ext 1
187
6
0
0
0
63
253 .723
per 4
3
301
3
0
3
71
385
.782
inn 0
1
2
370
0
0
59
432 .856
wal
0
0
0
0
42
1
3
46 .913
out 0
0
2
0
0
168
27
197 .853
colSUM 1697
187
315
374
42
172
387
3174  O.R. = .868
precision .997
.979
.958
.989
1.00
.977
S.C. = .878
  O.P. = .989
specificity
.996
.998
.995
.998
1.00
.999


O.S. = .997

Table: Confusion matrix for Nair & Rost (1161 proteins in Swiss-Prot)

Prd ⇒
Obs ⇓
nuc mit  ext  end  chl  cyt  pex  vac  lys  gol  N.P.  rowSUM  recall 
nuc
258
1
0
0
0
4
0
0
0
1
88
352
.733
mit
0
143
1
0
1
0
0
0
0
0
45
190
.753
ext
0
1
244
0
0
2
0
0
0
1
86
334
.731
end
0
0
0
9
0
0
0
0
0
0
5
14
.643
chl
0
0
0
1
58
1
0
0
0
0
34
94
.618
cyt
3
0
0
1
1
88
0
0
0
0
43
136
.647
pex
0
0
0
0
0
0
8
0
0
0
0
8
1.00
vac
0
1
0
0
0
1
0
2
0
0
0
4
.500
lys
0
0
0
0
0
0
0
0
7
0
0
7
1.00
gol
0
0
0
0
0
0
0
0
0
19
3
22
.865
colSUM 261
146
245
11
60
96
8
2
7
21
304
1161 O.R. = .720
precision .989
.979
.996
.818
.967
.917
1.00
1.00
1.00
.905
S.C. = .738
  O.P. = .975
specificity
.996
.997
.999
.998
.998
.992
1.00
1.00
1.00
.998


O.S. = .998

Table: Confusion matrix for PSORT-B data

Prd ⇒
Obs ⇓
cyt  inn  per  out  ext  N.P.  rowSUM  recall 
cyt 200
1
2
0
0
49
252 .794
inn 1
193
1
0
0
113
308 .627
per 3
3
192
2
3
61
264 .727
out 1
0
0
132
0
245
378 .349
ext 0
0
3
0
171
67
241 .71
colSUM 205
197
198
134
174
535
1443 O.R. = .615
precision .976
.980
.970
.985
.983
S.C. = .629
  O.P. = .978
specificity
.996
.996
.995
.999
.998


O.S. = .998