Work

Supplementary Material : "Machine learning decision tree-based models for predicting the antibacterial activity of Lamiaceae essential oils against Staphylococcus aureus"

Year: 2025

Type: supplementary-materials

Abstract: This study applies machine learning to predict the antibacterial activity of Lamiaceae essential oils (EOs) against Staphylococcus aureus. A curated dataset (NAEOL) was developed, incorporating chemical composition and MIC values. Decision tree models and artificial neural networks classified EOs as active or inactive, with J48 showing the best performance. Key terpenoids—Fenchone, (-)-α-Cubebene, β-Caryophyllene, Camphene, and Eucalyptol—were identified as antibacterial biomarkers, demonstrating the potential of computational approaches in bioactive EO discovery.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14913577

Authors: de Oliveira Miranda, Daniel, Santos Ferreira, Miller, Katchborian Neto, Albert, Almeida de Oliveira, Renato, Baldim Zanin, Joao Luiz, Branquinho Oliveira, Tiago, Ferreira Dias, Danielle, Chagas-Paula, Daniela, Gomes Soares, Marisi

Institutions: Universidade Federal de Alfenas, Universidade Federal do ABC, Instituto Federal do Sul de Minas, Universidade Federal de Sergipe

Language: English

Topic: Essential Oils and Antimicrobial Activity

Subfield: Food Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W6949445065

Cites:

Cited by:

Related to: