ZooxClassML: Evaluation of Machine Learning Models for Classification of Live and Dead Symbiodiniaceae algae

Ariza Rangel, María Alejandra

Publicación:
ZooxClassML: Evaluation of Machine Learning Models for Classification of Live and Dead Symbiodiniaceae algae

authorProfile.id.code	201630525
dc.contributor.advisor	Sánchez Muñoz, Juan Armando
dc.contributor.author	Ariza Rangel, María Alejandra
dc.contributor.jury	Nuñez Castro, Haydemar Maria
dc.contributor.researchgroup	Facultad de Ciencias::Biología Molecular Marina -Biommar
dc.date.accessioned	2025-06-24T16:09:03Z
dc.date.available	2025-06-24T16:09:03Z
dc.date.issued	2025-06-02
dc.description.abstract	Coral reefs are key contributors to marine biodiversity, and their survival depends on the symbiotic relationship between corals and Symbiodiniaceae algae. One of the main natural dispersers of these microalgae is the parrotfish, which releases them through its feces. Therefore, identifying which cells in fecal samples correspond to Symbiodiniaceae, and more importantly, determining their viability, is crucial for understanding coral reef ecosystems. Flow cytometry is a powerful tool for analyzing cellular characteristics at the single-cell level, but manual classification of these cells is time-consuming and subject to human bias. This study explores the use of machine learning (ML) to automate the classification of algae and sediment particles in parrotfish fecal samples. Initial clustering was carried out using unsupervised algorithms (K-Means and Gaussian Mixture Models), with K-Means producing clearer separation in PCA-reduced feature space. These clusters were used to train supervised models, including Support Vector Machines (SVM), Random Forest and HistGradientBoosting. While models performed similarly, HistGradientBoosting was selected due to its faster training time and comparable accuracy. The models successfully captured biologically meaningful patterns, including the gradual transition from viable to non-viable cells and the accurate identification of sediment particles. This study highlights the value of combining flow cytometry with machine learning to support ecological research and conservation of coral reef environments.	eng
dc.description.degreelevel	Maestría
dc.format.extent	23 paginas
dc.format.mimetype	application/pdf
dc.identifier.instname	instname:Universidad de los Andes
dc.identifier.reponame	reponame:Repositorio Institucional Séneca
dc.identifier.repourl	repourl:https://repositorio.uniandes.edu.co/
dc.identifier.uri	https://hdl.handle.net/1992/76370
dc.language.iso	eng
dc.publisher	Universidad de los Andes
dc.publisher.department	Departamento de Ciencias Biológicas
dc.publisher.faculty	Facultad de Ciencias
dc.publisher.program	Maestría en Biología Computacional
dc.rights.accessrights	info:eu-repo/semantics/openAccess
dc.rights.coar	http://purl.org/coar/access_right/c_abf2
dc.rights.uri	https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
dc.subject.keyword	Machine learning	eng
dc.subject.keyword	Flow cytometry	eng
dc.subject.keyword	Symbiodiniaceae algae	eng
dc.subject.keyword	Parrotfish feces	eng
dc.subject.keyword	Cell viability	eng
dc.subject.keyword	Unsupervised learning	eng
dc.subject.keyword	Supervised classification	eng
dc.subject.themes	Biología	spa
dc.title	ZooxClassML: Evaluation of Machine Learning Models for Classification of Live and Dead Symbiodiniaceae algae	eng
dc.type	Trabajo de grado - Maestría
dc.type.coar	http://purl.org/coar/resource_type/c_bdcc
dc.type.coarversion	http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.content	Text
dc.type.driver	info:eu-repo/semantics/masterThesis
dc.type.redcol	https://purl.org/redcol/resource_type/TM
dc.type.version	info:eu-repo/semantics/acceptedVersion
dspace.entity.type	Publication
person.identifier.cvlac	https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000057240
person.identifier.gsid	https://scholar.google.es/citations?user=l8nad3oAAAAJ
person.identifier.orcid	0000-0001-7149-8369
relation.isDirectorOfPublication	6652e019-e472-4748-9dc3-b125c616a2a4
relation.isDirectorOfPublication.latestForDiscovery	6652e019-e472-4748-9dc3-b125c616a2a4

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Nombre:: ZooxClassML: Evaluation of Machine Learning Models for Classification of Live and Dead Symbiodiniaceae algae.pdf
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