Compression and k-Mer Based Approach for Anticancer Peptide Analysis

Anti-cancer peptide (ACP) sequence classification is crucial for cancer treatment development. Current neural network approaches achieve high accuracy but require substantial parameters and training data. Recent compression-based methods compress entire sequences, potentially missing fine-grained neighboring information critical for classification. We propose a novel approach integrating Gzip compression with an incremental k-mer strategy. Unlike conventional methods, we compress individual k-mers and incrementally build subsequence compressions, preserving amino acid-level context. Using Normalized Compression Distance (NCD) and kernel-based embeddings, our parameter-free method achieves state-of-the-art performance on breast and lung cancer ACP datasets, outperforming deep neural networks and large language models without requiring custom features or pre-trained models. Our approach provides a practical, efficient alternative to computationally intensive methods, proving effective even in low-resource environments.