Comprehensive and deep learning classification for analyses of the biological complexity of growth and biofilms of Cobetia marina under different temperature growths
This study demonstrates the effectiveness of advanced computational approaches in analyzing biological complexity. The integration of deep learning methods with comprehensive experimental design provides a robust framework for understanding microbial behavior under varying environmental conditions, with potential applications in biotechnology and environmental monitoring.
Authors
- Karoll González-Pizarro (ORCID: https://orcid.org/0000-0002-3592-6028)
- Milan Stehlík (ORCID: https://orcid.org/0000-0003-2749-5990)
- Claudia Ibacache‐Quiroga (ORCID: https://orcid.org/0000-0002-1978-6109)
- Jozef Kiseľák (ORCID: https://orcid.org/0000-0001-6450-4601)
- M. Alejandro Dinamarca (ORCID: https://orcid.org/0000-0002-5228-7686)
- Bastián Barraza-Morales
- Benjamín León Schuffeneger
Institutions
- University of Pavol Jozef Šafárik (SK)
- Institute of Mathematics (BY)
- University of Valparaíso (CL)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-12-11
- DOI
- https://doi.org/10.1371/journal.pone.0336575
- Primary Topic
- Bacterial biofilms and quorum sensing
- Type
- article
- Field-Weighted Citation Impact
- 0.00