A scalable method for extracting features using a complex network from SNP sequences and a novel scalable max of min algorithm for clustering

Abstract

Feature extraction is pivotal in bioinformatics as it converts variable-length genome sequences into fixed-length mathematical feature vectors, which serve as input for clustering algorithms to cluster similar sequences. One of the types of genome sequences is the Single Nucleotide Polymorphism (SNP), which categorises individuals into risk categories for diseases and predicts treatment outcomes more reliably. Extracting features with current state-of-the-art approaches from SNP sequences poses many challenges, including extracting similar features for distinct sequences and lacking context-based features. These approaches also take enormous time to compute features for a huge amount of SNP sequences. Therefore, a scalable approach to extract features is proposed based on a complex network, which converts the real-life SNP sequences (collected from ICAR-Indian Institute of Soybean Research Indore) into the complex network and extracts the proposed relevant features.