  Species living in the extreme cold environment fight against the harsh
conditions using antifreeze proteins (AFPs), that manipulates the freezing
mechanism of water in more than one way. This amazing nature of AFP turns out
to be extremely useful in several industrial and medical applications. The lack
of similarity in their structure and sequence makes their prediction an arduous
task and identifying them experimentally in the wet-lab is time-consuming and
expensive. In this research, we propose a computational framework for the
prediction of AFPs which is essentially based on a sample-specific
classification method using the sparse reconstruction. A linear model and an
over-complete dictionary matrix of known AFPs are used to predict a sparse
class-label vector that provides a sample-association score. Delta-rule is
applied for the reconstruction of two pseudo-samples using lower and upper
parts of the sample-association vector and based on the minimum recovery score,
class labels are assigned. We compare our approach with contemporary methods on
a standard dataset and the proposed method is found to outperform in terms of
Balanced accuracy and Youden's index. The MATLAB implementation of the proposed
method is available at the author's GitHub page
(\{https://github.com/Shujaat123/AFP-SRC}{https://github.com/Shujaat123/AFP-SRC}).
