Machine learning in anthelmintics testing

31/05/2022

The possibilities and advantages of automated video processing were validated by students in their computational/experimental work published in the prestigious Computational and Structural Biotechnology Journal (IF 7,271; QIF1, QAIS1 published by Elsevier).

Computational and Structural Biotechnology Journal

One of the few methods for anthelmintic testing is to monitor their effect on the motility of parasitic nematode larvae. The classical evaluation of such an experiment comprises microscopic observation and subjective motility scoring. Automation using an algorithm based on machine learning expands the current boundaries in the assessment of this methods and facilitates the testing of new potential anthelmintics. The main authors of the publication are postgraduate students from the Research Group for the Study of Xenobiotic Resistance Mechanisms Mgr. Martin Žofka and Mgr. Linh Thuy Nguyen. Congratulations.

Publication available: https://doi.org/10.1016/j.csbj.2022.05.014

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Text: Assoc. Prof. Ing. Petra Matoušková, Ph.D.
Graphics: Research Group for the Study of Xenobiotic Resistance Mechanisms

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