AFP-SRC: Identification of Antifreeze Proteins Using Sparse Representation Classifier
This work addresses a domain-specific problem for biologists and industries needing efficient AFP identification, but it is incremental as it builds on existing sparse representation techniques.
The researchers tackled the problem of identifying antifreeze proteins (AFPs), which are difficult to predict due to structural and sequence diversity, by proposing a computational framework based on sparse representation classification. The method outperformed contemporary approaches on a standard dataset, achieving higher balanced accuracy and Youden's index.
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}).