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After completing the calculations, we carried out our analysis of the hinge finding
algorithms on two levels: the quantitative level, which consisted of examining various
statistical benchmarks, and the qualitative level, which consisted of interpreting results
for individual proteins. In this section we will discuss the quantitative summaries and
will also point out advantages and disadvantages of the algorithms through the specific
examples presented in here and in the supplementary information. In this section as
before we will focus on StoneHinge, TLSMD, hNMb, and FO, and to a lesser extent
hNMd, only intermittently referring to the various other subsidiary predictors and
HingeSeq.
Statistical benchmarks
We begin to get an idea of the predictive value of the various methods from the weight
assigned to each predictor, in Table 4.2. Here we see that hNMb and FO receive by far
the highest weights, hinting at high predictive power, while TLSMD and StoneHinge get
a more moderate weight and hNMd gets almost no weight. This cannot be taken as a
rigorous statistical benchmark, however, since the predictors are not independent, and
some, such as FO and FO1M, are in fact very closely related. For greater rigor, we
computed the sensitivity, specificity, and p-value associated with each predictor in Table
4.3. These statistical measures are explained in detail in prior work.
The sensitivity (true positives/gold standard positives) was highest for FO, followed by
Stonehinge, TLSMD, and hNMb. FO was thus able to find the largest number of hinge
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