Characteristic Attribute Organization System (CAOS): Identifying Classification Rules Based on Phylogenetically Organized Sequences

Vivek Ramanan; Indra Neil Sarkar

doi:10.1007/978-1-0716-3581-0_21

Characteristic Attribute Organization System (CAOS): Identifying Classification Rules Based on Phylogenetically Organized Sequences

Methods Mol Biol. 2024:2744:335-345. doi: 10.1007/978-1-0716-3581-0_21.

Authors

Vivek Ramanan^{1

2}, Indra Neil Sarkar^{3

4}

Affiliations

¹ Center of Computational Molecular Biology Brown University, Providence, RI, USA.
² Center for Biomedical Informatics Brown University, Providence, RI, USA.
³ Center of Computational Molecular Biology Brown University, Providence, RI, USA. neil_sarkar@brown.edu.
⁴ Center for Biomedical Informatics Brown University, Providence, RI, USA. neil_sarkar@brown.edu.

PMID: 38683329
DOI: 10.1007/978-1-0716-3581-0_21

Abstract

Classification is a technique that labels subjects based on the characteristics of the data. It often includes using prior learned information from preexisting data drawn from the same distribution or data type to make informed decisions per each given subject. The method presented here, the Characteristic Attribute Organization System (CAOS), uses a character-based approach to molecular sequence classification. Using a set of aligned sequences (either nucleotide or amino acid) and a maximum parsimony tree, CAOS will generate classification rules for the sequences based on tree structure and provide more interpretable results than other classification or sequence analysis protocols. The code is accessible at https://github.com/JuliaHealth/CAOS.jl/ .

Keywords: CAOS; Classification; DNA barcoding; Diagnosis.

MeSH terms

Algorithms
Computational Biology / methods
Phylogeny*
Sequence Alignment / methods
Software*