Machine Learning Applied to Fluorescence Microscopy Images to Assist the Development of Malaria Transmission-Blocking Drugs

Summary

Malaria is a serious disease and one of the major health challenges in the world today. African countries are particularly badly affected, carrying the overwhelming majority of malaria cases. Children under 5 are the most vulnerable.

Therefore, the development of new types of drugs, such as transmission-blocking drugs (TBDs), is crucial to handling the problem. Unlike traditional drugs, TBDs prevent transmission by targeting Plasmodium (the parasite responsible for malaria transmission).

Analysis of fluorescence microscopy images is one way to assist in the development of TBDs. Fluorescence microscopy is often used to trace small features, such as microbes or chemical compounds, in human cells. It is used to estimate how drugs affect the transmission of Plasmodium.

In the past decade, machine learning (and deep learning methods in particular) have become increasingly popular in image analysis. This also includes the analysis of the Fluorescence Microscopy Images.

The project will focus on developing and evaluating novel ML-based methods to analyse fluorescence microscopy images of cells affected by Malaria and/or TBDs to test their efficiency. The methods will be tested using real-life biological data provided by one of the co-supervisors.

The project will be done in collaboration with experts from LSHTM and ETH Zurich.

References

[1] Tsebriy O, Khomiak A, Miguel-Blanco C, Sparkes PC, Gioli M, et al. (2023) Machine learning-based phenotypic imaging to characterise the targetable biology of Plasmodium falciparum male gametocytes for the development of transmission-blocking antimalarials. PLOS Pathogens 19(10): e1011711. https://doi.org/10.1371/journal.ppat.1011711

[2] Juan C Caicedo, Jonathan Roth, Allen Goodman, Tim Becker, Kyle W Karhohs, Matthieu Broisin, Csaba Molnar, Claire McQuin, Shantanu Singh, Fabian J Theis, and Anne E Carpenter. Evaluation of deep learning strategies for nucleus segmentation in fluorescence images. Cytometry A, 95(9): 952-965, July 2019. https://doi.org/10.1002/cyto.a.23863

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