Membrane Clustering of Coronavirus Variants Using Document Similarity

Area of Science:

• Bioinformatics and Computational Biology
• Genomics
• Membrane Computing

Background:

• Bioinformatics and genomic analysis are increasingly important, accelerated by the COVID-19 pandemic.
• Genomes can be represented as character strings, enabling the application of document similarity metrics.
• P systems (membrane systems) are computational models inspired by cellular processes, applicable to data clustering.

Purpose of the Study:

• To present a novel and versatile clustering method for genome data.
• To explore the utilization of membrane computing models for genome clustering.
• To apply document similarity metrics within membrane clustering for genome analysis.

Main Methods:

• Representing viral genomes as character strings based on nucleobases.
• Applying document similarity metrics to quantify genome similarities.
• Utilizing P systems (membrane systems) for clustering the similarity results.

Main Results:

• Demonstrated a novel application of membrane clustering models for genome data.
• Successfully applied document similarity metrics within a membrane computing framework.
• Provided a new method for analyzing and clustering genomic sequences.

Conclusions:

• The proposed membrane clustering method offers a versatile approach for genome analysis.
• This study highlights an under-explored area: the use of membrane systems with document similarity for genome clustering.
• The findings contribute to advancing bioinformatics tools for genomic research.