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!
!
while the set of residues in the test set we call {TEST} . We used Equation 7 with the
training set data x, y(i | i " {TRAINING}) to obtain "
!
* , the cross-validation value of " .
We then used this vector with the individual predictor results for residues in the test set to
!
obtain cross-validated HingeMaster results !
*
x HingeMaster
*
x HingeMaster
!
(i | i " {TEST}) = # * $ x(i | i " {TEST}) [8]
180
(i | i " {TEST})
as follows: !
We then generated a ROC curve by gradually decreasing the threshold above which
values of
*
x HingeMaster(i
| i " {TEST}) were taken to correspond to predicted hinge locations,
and comparing these to the annotated hinge locations
!
of the threshold, residues i with scores
y(i | i " {TEST}). For each value
*
x HingeMaster(i)
above that threshold are taken to be
!
test positives. We further classify the test positives using a strict criterion, meaning that
those that coincide exactly with ! annotated hinges ( y(i) =1) are taken as true positives,
those that coincide with non-hinge residues ( y(i) = 0) are taken as false positives, even if
they are immediately adjacent to a hinge
!
residue. We will later use a loose criterion for a
more qualitative measure of success. ! The generation of the ROC curve is explained in
more detail in prior work.
We repeated the above process a total of 20 times. Each time, we generated new {TEST}
and {TRAINING} sets by randomly dividing HAG as described. We obtained 20
!