  In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs)
to counter the otherwise lethal intracellular formation of ice. Structures and
sequences of various AFPs exhibit a high degree of heterogeneity, consequently
the prediction of the AFPs is considered to be a challenging task. In this
research, we propose to handle this arduous manifold learning task using the
notion of localized processing. In particular an AFP sequence is segmented into
two sub-segments each of which is analyzed for amino acid and di-peptide
compositions. We propose to use only the most significant features using the
concept of information gain (IG) followed by a random forest classification
approach. The proposed RAFP-Pred achieved an excellent performance on a number
of standard datasets. We report a high Youden's index
(sensitivity+specificity-1) value of 0.75 on the standard independent test data
set outperforming the AFP-PseAAC, AFP\_PSSM, AFP-Pred and iAFP by a margin of
0.05, 0.06, 0.14 and 0.68 respectively. The verification rate on the UniProKB
dataset is found to be 83.19\% which is substantially superior to the 57.18\%
reported for the iAFP method.
