DeepVISP (Deep learning for viral integration sites prediction)

  Visualization of the kernel-specific motifs of the first convolutional layer in DeepVISP and distribution of PWMs of each motif.

The visualizations are based on the benchmark data set. First, we need the output of the first convolutional layer given the DNA sequences. This output is called 'activations'. You can think of a kernel as a position-weight matrix (PWM) and activations are the result of sliding with the kernel over an input sequence, one position at a time. The result is a vector of numbers and the position of the maximum activation shows to which subsequence the kernel is most similar. If the maximum activation is above a certain threshold we can extract the corresponding subsequence of the length of the kernel from the input starting at that position. If we do this for all input sequences we get a number of subsequences, all of the same length, and we can compute a PWM. In addition, we drew the heatmaps to show the distributions of the PWMs for each motif.

Motifs score. Motifs logo. Distribution of the PWMs.


Kernel 89
score = 0.011


Kernel 55
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Kernel 91
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Kernel 219
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Kernel 82
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Kernel 147
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Kernel 85
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Kernel 184
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Kernel 212
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Kernel 80
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Kernel 90
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Kernel 48
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Kernel 127
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Kernel 6
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Kernel 194
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Kernel 94
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Kernel 236
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Kernel 225
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Kernel 211
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Kernel 5
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Kernel 164
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Kernel 193
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Kernel 136
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Kernel 205
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Kernel 88
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Kernel 65
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Kernel 70
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Kernel 126
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Kernel 114
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Kernel 11
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Kernel 176
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Kernel 191
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Kernel 10
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Kernel 116
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Kernel 160
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Kernel 166
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Kernel 203
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Kernel 46
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Kernel 35
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Kernel 179
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Kernel 231
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Kernel 239
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Kernel 44
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Kernel 123
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Kernel 220
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Kernel 69
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Kernel 209
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Kernel 140
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Kernel 97
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Kernel 34
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Kernel 109
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Kernel 198
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Kernel 32
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Kernel 235
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Kernel 113
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Kernel 93
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Kernel 172
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Kernel 124
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Kernel 59
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Kernel 135
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Kernel 167
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Kernel 106
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Kernel 131
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Kernel 208
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Kernel 125
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Kernel 125
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Kernel 95
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Kernel 25
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Kernel 77
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Kernel 175
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Kernel 177
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Kernel 151
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Kernel 159
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Kernel 84
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Kernel 201
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Kernel 204
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Kernel 215
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Kernel 207
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Kernel 120
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Kernel 143
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Kernel 190
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Kernel 108
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Kernel 71
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Kernel 102
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Kernel 39
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Kernel 133
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Kernel 165
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Kernel 188
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Kernel 107
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Kernel 0
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Kernel 217
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Kernel 155
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Kernel 105
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Kernel 174
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Kernel 110
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Kernel 180
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Kernel 41
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Kernel 75
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Kernel 20
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Kernel 92
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Kernel 23
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Kernel 24
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Kernel 148
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Kernel 19
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Kernel 7
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Kernel 2
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Kernel 42
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Kernel 137
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Kernel 183
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Kernel 43
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Kernel 118
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Kernel 17
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Kernel 171
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Kernel 185
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Kernel 121
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Kernel 62
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Kernel 139
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Kernel 168
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Kernel 26
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Kernel 68
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Kernel 162
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Kernel 169
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Kernel 54
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Kernel 61
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Kernel 158
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Kernel 195
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Kernel 38
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Kernel 154
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Kernel 218
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Kernel 138
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Kernel 226
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Kernel 112
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Kernel 101
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Kernel 87
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Kernel 233
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Kernel 50
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Kernel 73
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Kernel 63
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Kernel 99
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Kernel 18
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Kernel 51
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Kernel 56
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Kernel 224
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Kernel 178
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Kernel 214
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Kernel 33
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Kernel 30
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Kernel 28
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Kernel 115
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Kernel 53
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Kernel 142
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Kernel 104
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Kernel 187
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Kernel 103
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Kernel 210
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Kernel 74
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Kernel 76
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Kernel 4
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Kernel 96
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Kernel 200
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Kernel 8
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Kernel 156
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Kernel 1
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Kernel 202
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Kernel 9
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Kernel 228
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Kernel 58
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Kernel 100
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Kernel 117
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Kernel 67
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Kernel 189
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Kernel 64
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Kernel 229
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Kernel 83
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Kernel 130
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Kernel 45
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Kernel 197
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Kernel 27
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Kernel 60
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Kernel 216
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Kernel 157
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Kernel 13
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Kernel 170
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Kernel 119
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Kernel 145
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Kernel 196
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Kernel 72
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Kernel 98
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Kernel 78
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Kernel 128
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Kernel 150
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Kernel 221
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Kernel 16
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Kernel 152
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Kernel 111
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Kernel 47
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Kernel 86
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Kernel 37
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Kernel 161
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Kernel 40
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Kernel 52
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Kernel 186
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Kernel 206
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Kernel 237
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Kernel 234
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Kernel 163
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Kernel 129
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Kernel 141
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Kernel 223
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Kernel 21
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Kernel 81
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Kernel 57
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Kernel 134
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Kernel 29
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Kernel 149
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Kernel 122
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Kernel 14
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Kernel 232
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Kernel 230
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Kernel 222
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Kernel 49
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Kernel 153
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Kernel 182
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Kernel 146
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Kernel 31
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Kernel 15
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Kernel 238
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Kernel 36
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Kernel 66
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Kernel 12
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Kernel 192
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Kernel 22
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Kernel 227
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Kernel 3
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Kernel 199
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Kernel 79
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Kernel 144
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Kernel 173
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Kernel 181
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Kernel 213
score = 0.000


Kernel
score =