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Taxa Identifation with Machine Learning Enhanced by DNA Metabarcoding (TIMED)

Project description

Taxa Identification with Machine Learning Enhanced by DNA Metabarcoding (TIMED) proposes combining DNA metabarcoding and optical machine learning in a novel interleaved way to pave the way toward automated aquatic biomonitoring. We will optimize the overall taxa identification pipeline consisting of imaging, metabarcoding, and optical machine learning blocks and develop novel state-of-the-art machine learning algorithms to three main tasks: 1) identification of taxa in the training set, 2) detection of taxa not in the training set, and 3) biomass estimation. For all of these tasks, we will consider novel ways to exploit the DNA metabarcoding output. During TIMED, we will collect a large-scale dataset suitable for advancing the proposed result. 

People in project

PI: Senior Research Scientist Jenni Raitoharju, YTO

Main researcher: Mikko Impiö, YTO

Other support at SYKE: 

Director Kristian Meissner, YTO

Senior Research Scientist Eero Siivola, YTO

International collaborators:

Assoc. Prof. Alexandros Iosifidis, Aarhus University, Denmark, expertise in project: machine learning

Senior Researcher Toke T. Høye, Aarhus University, Denmark, expertise in project: ecology 

Prof. Florian Leese, University of Duisburg-Essen, Germany, expertise in project: DNA metabarcoding

More information

Senior Research Scientist Jenni Raitoharju

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Project Stage
Taxa Identifation with Machine Learning Enhanced by DNA Metabarcoding (TIMED)

TIMED proposes combining DNA metabarcoding and optical machine learning in a novel interleaved way to pave the way toward automated aquatic biomonitoring. We will optimize the overall taxa identificat...

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