Compression and k-mer based Approach For Anticancer Peptide Analysis

Our research delves into the imperative realm of anti-cancer peptide sequence analysis, an essential domain for biological researchers. Presently, neural network-based methodologies, while exhibiting precision, encounter challenges with a substantial parameter count and extensive data requirements. The recently proposed method to compute the pairwise distance between the sequences using the compression-based approach [26] focuses on compressing entire sequences, potentially overlooking intricate neighboring information for individual characters (i.e., amino acids in the case of protein and nucleotide in the case of nucleotide) within a sequence. The importance of neighboring information lies in its ability to provide context and enhance understanding at a finer level within the sequences being analyzed. Our study advocates an innovative paradigm, where we integrate classical compression algorithms, such as Gzip, with a pioneering k-mersbased strategy in an incremental fashion. Diverging from conventional techniques, our method entails compressing individual k-mers and incrementally constructing the compression for subsequences, ensuring more careful consideration of neighboring information for each character. Our proposed method improves classification performance without necessitating custom features or pre-trained models. Our approach unifies compression, Normalized Compression Distance, and k-mers-based techniques to generate embeddings, which are then used for classification. This synergy facilitates a nuanced understanding of cancer sequences, surpassing state-of-the-art methods in predictive accuracy on the Anti-Cancer Peptides dataset. Moreover, our methodology provides a practical and efficient alternative to computationally demanding Deep Neural Networks (DNNs), proving effective even in low-resource environments.