Events News Research CBS CBS Publications Bioinformatics
Staff Contact About Internal CBS CBS Other

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



GETTING HELP

Scientific problems:        Technical problems: