Output format
DESCRIPTION
Example of output is found below. The output is divided into the folowinng sections:
- Description of training data
- Prediction method
- Prediction data
- Evaluation of predictions
- Predictions
This section contain a line "Number Sequence (Assignment) Prediction"
followed by the predictions in 4 collumns:
1. Residue number
2. Sequence of peptide that the prediction is made on
3. Assignment of the correct output (if made available by the user)
4. Predicted value
EXAMPLE OUTPUT
Description of training data
Length of motif: 9
Number of training data: 200
Threshold for counting example as positive: 0.500000
Prediction method.
Neural network
Number of input units: 180
Number of hidden units: 2
Number of bins used for balancing training: 2
Doing 5 fold cross validation
Cross validation number 1
Output from the neural network program (HOW)
Maximal test set correlation coefficent sum = 0.323400 in epoch 255
Maximal test set pearson correlation coefficent sum = 0.329300 in epoch 300
minimal per example squared error = 0.039100 in epoch 299
Cross validation number 2
Output from the neural network program (HOW)
Maximal test set correlation coefficent sum = 0.397700 in epoch 239
Maximal test set pearson correlation coefficent sum = 0.514800 in epoch 299
minimal per example squared error = 0.017400 in epoch 299
Cross validation number 3
Output from the neural network program (HOW)
Maximal test set correlation coefficent sum = 0.285700 in epoch 256
Maximal test set pearson correlation coefficent sum = 0.441000 in epoch 300
minimal per example squared error = 0.027800 in epoch 272
Cross validation number 4
Output from the neural network program (HOW)
Maximal test set correlation coefficent sum = 0.369800 in epoch 282
Maximal test set pearson correlation coefficent sum = 0.561800 in epoch 225
minimal per example squared error = 0.021600 in epoch 300
Cross validation number 5
Output from the neural network program (HOW)
Maximal test set correlation coefficent sum = 0.315100 in epoch 208
Maximal test set pearson correlation coefficent sum = 0.546600 in epoch 261
minimal per example squared error = 0.021800 in epoch 272
Parameters for prediction method
Prediction data
Number of evaluation data: 66
Predicting using a neural network
Using all networks
Evaluation of predictions
Pearson coefficient for N= 66 data: 0.53066
Aroc value: 0.77124
Predictions
Number Sequence Assignment Prediction
1 ILYQVPFSV 0.853 0.696
2 VVMGTLVAL 0.589 0.542
3 ILDEAYVMA 0.494 0.608
4 KILSVFFLA 0.851 0.526
5 HLYQGCQVV 0.539 0.558
6 YLDLALMSV 0.843 0.689
7 ALAKAAAAA 0.563 0.499
8 MALLRLPLV 0.634 0.555
9 FLLTRILTI 0.803 0.586
10 ILSSLGLPV 0.638 0.533
11 RMYGVLPWI 0.689 0.621
12 ALPYWNFAT 0.323 0.575
13 YLEPGPVTV 0.647 0.665
14 FLPWHRLFL 0.564 0.556
15 LLPSLFLLL 0.554 0.516
16 MLQDMAILT 0.527 0.542
17 LVSLLTFMI 0.301 0.423
18 GLMTAVYLV 0.798 0.592
19 ILTVILGVL 0.451 0.473
20 GLYSSTVPV 0.697 0.620
21 SLYFGGICV 0.782 0.500
22 GLYYLTTEV 0.719 0.595
23 ALYGALLLA 0.818 0.669
24 IMPGQEAGL 0.614 0.580
25 WLSLLVPFV 0.822 0.560
26 YLVAYQATV 0.639 0.645
27 RLMIGTAAA 0.499 0.525
28 WLDQVPFSV 0.774 0.657
29 AAAKAAAAV 0.446 0.450
30 KTWGQYWQV 0.778 0.575
31 VIHAFQYVI 0.343 0.407
32 GLLGWSPQA 0.793 0.585
33 YMLDLQPET 0.654 0.599
34 HLAVIGALL 0.571 0.449
35 MLLAVLYCL 0.463 0.614
36 MMWYWGPSL 0.770 0.552
37 FVNHDFTVV 0.473 0.479
38 FLLRWEQEI 0.700 0.563
39 IIDQVPFSV 0.659 0.646
40 QVMSLHNLV 0.367 0.435
41 SVYVDAKLV 0.572 0.456
42 RLLDDTPEV 0.578 0.605
43 IAATYNFAV 0.581 0.515
44 YLVSFGVWI 0.941 0.520
45 ILLLCLIFL 0.541 0.569
46 AIAKAAAAV 0.399 0.474
47 LLLCLIFLL 0.699 0.530
48 GLQDCTMLV 0.710 0.578
49 ALAKAAAAL 0.470 0.492
50 MLGNAPSVV 0.499 0.561
51 FTDQVPFSV 0.619 0.652
52 YLAPGPVTA 0.794 0.649
53 GLLGNVSTV 0.706 0.586
54 GTLGIVCPI 0.503 0.531
55 YLEPGPVTI 0.614 0.633
56 LLFLGVVFL 0.638 0.542
57 SLAGFVRML 0.565 0.528
58 GLYLSQIAV 0.578 0.578
59 WTDQVPFSV 0.392 0.611
60 RLTEELNTI 0.374 0.499
61 KLTPLCVTL 0.572 0.586
62 YLYPGPVTA 0.739 0.695
63 TVLRFVPPL 0.599 0.506
64 ILSPFMPLL 0.648 0.584
65 FVWLHYYSV 0.749 0.573
66 ILDQVPFSV 0.635 0.677