Skip to main content

Table 1 Trial and ErrorOptimization of BPNN with only functional SNPs

From: Optimizationof neural network architecture using genetic programming improvesdetection and modeling of gene-gene interactions in studies of humandiseases

HL U/L M Epistasis Models
    1 2 3 4 5
0 0 .1 0.48786 0.49460 0.49560 0.49160 0.49545
0 0 .5 0.48786 0.49460 0.49560 0.49160 0.49545
0 0 .9 0.48786 0.49460 0.49560 0.49160 0.49545
1 5 .1 0.47317 0.45883 0.49568 0.49160 0.49553
1 5 .5 0.36422 0.34229 0.48754 0.49010 0.49543
1 5 .9 0.31206 0.23181 0.34522 0.44670 0.48905
1 10 .1 0.47430 0.46820 0.49607 0.49150 0.49559
1 10 .5 0.35916 0.36446 0.49284 0.49020 0.49542
1 10 .9 0.31209 0.23193 0.34524 0.44660 0.49136
1 15 .1 0.48495 0.47508 0.49599 0.49160 0.49552
1 15 .5 0.37511 0.36221 0.49364 0.49150 0.49542
1 15 .9 0.31217 0.23203 0.34525 0.44670 0.49399
1 20 .1 0.48630 0.49240 0.49583 0.49160 0.49549
1 20 .5 0.40750 0.34406 0.49469 0.49070 0.49544
1 20 .9 0.31217 0.23216 0.34511 0.44660 0.49402
2 5:5 .1 0.49965 0.49997 0.49997 0.50000 0.49996
2 5:5 .5 0.49628 0.49980 0.49996 0.49990 0.49995
2 5:5 .9 0.31205 0.23704 0.41740 0.44670 0.49471
2 10:5 .1 0.49623 0.49980 0.49987 0.49980 0.49972
2 10:5 .5 0.49024 0.49854 0.49929 0.49950 0.49847
2 10:5 .9 0.31201 0.23158 0.35430 0.45450 0.49477
2 15:5 .1 0.49398 0.49944 0.49954 0.49940 0.49913
2 15:5 .5 0.48697 0.49578 0.49850 0.49530 0.49700
2 15:5 .9 0.31199 0.23584 0.35993 0.44740 0.49465
2 20:5 .1 0.49160 0.49849 0.49946 0.49840 0.49889
2 20:5 .5 0.48700 0.49212 0.49808 0.49290 0.49596
2 20:5 .9 0.31199 0.23157 0.34657 0.44750 0.49519
  1. Results from the trial and error optimizationof the BPNN on one dataset from each epistasis model. We used 27different architectures varying in HL – hidden layer, U/L – unitsper layer, M – momentum. The average classification error across10 cross-validations from each data set generated for each of thefive epistasis models are shown. The best architecture is shownin bold and in Figure 5. Thiswas the most parsimonious architecture with the minimum classificationerror.