Weighted Chaos Game Representation for Molecular Sequence Classification

Molecular sequence analysis is a crucial task in bioinformatics and has several applications in drug discovery and disease diagnosis. However, traditional methods for molecular sequence classification are based on sequence alignment, which can be computationally expensive and lack accuracy. Although alignment-free methods exist, they usually do not take full advantage of deep learning (DL) models since DL models traditionally perform below power on tabular data compared to their effectiveness on image-based data. To address this, we propose a novel approach to classify molecular sequences using a Chaos Game Representation (CGR)-based approach. We utilize k-mers-based frequency chaos game representation (FCGR) to generate 2D images for molecular sequences. Additionally, we incorporate scaling features for the sliding windows, including Kyte and Doolittle (KD) hydropathy scale, Eisenberg hydrophobicity scale, Hydrophilicity scale, Flexibility of the characters, and Hydropathy scale, to assign weights to the k-mers. By selecting multiple features, we aim to improve the accuracy of molecular sequence classification models. The motivations to incorporate weights for the k-mers in the molecular sequence analysis are the fact that different k-mers may have different levels of importance or relevance to the classification task at hand and that incorporating additional information, such as hydropathy scales, could improve the accuracy of classification models. The proposed method shows promising results in molecular sequence classification by outperforming the baseline methods and provides a new direction for analyzing sequences using image classification techniques.