Many of the recent secondary structure prediction methods incorporate the idea of fuzzy set theory, where instead of assigning a definite
secondary structure to a query residue, probability for the residue being in each of the conformational states is estimated. Moreover,
continuous assignment of conformational states to the experimentally observed protein structures can be performed, in order to reflect
inherent flexibility. Although various measures have been developed for evaluating performances of secondary structure prediction methods,
they depend only on the most probable secondary structures. They do not assess the accuracy of the probabilities produced by fuzzy
prediction methods, and they cannot incorporate information contained in continuous assignments of conformational states to observed
structures. Three important measures for evaluating performance of a secondary structure prediction algorithm, Q score, Segment OVerlap
(SOV) measure, and the k-state correlation coefficient (Corr), are deformed into fuzzy measures F score, Fuzzy OVerlap (FOV) measure, and
the fuzzy correlation coefficient (Forr), so that the new measures not only assess probabilistic outputs of fuzzy prediction methods, but also
incorporate information from continuous assignments of secondary structure. As an example of application, prediction results of four fuzzy
secondary structure prediction methods, PSIPRED, PROFking, SABLE, and PREDICT, are assessed using the new fuzzy measures.