Imperial College London
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Professor Jake Baum

Faculty of Natural Sciences, Department of Life Sciences

Visiting Professor
 
 
 
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Contact

 

+44 (0)20 7594 5420jake.baum Website

 
 
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Location

 

c/o Baum labSir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Ashdown:2019:10.1101/2019.12.19.882480,
author = {Ashdown, GW and Dimon, M and Fan, M and Terán, FS-R and Witmer, K and Gaboriau, DCA and Armstrong, Z and Hazard, J and Ando, DM and Baum, J},
doi = {10.1101/2019.12.19.882480},
title = {A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens},
url = {http://dx.doi.org/10.1101/2019.12.19.882480},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - <jats:title>Abstract</jats:title><jats:p>Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. Such methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human- and machine-labelled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semi-supervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Our approach also reproducibly detects and clusters drug-induced morphological outliers by mechanism of action, demonstrating the potential power of machine learning for accelerating cell-based drug discovery.</jats:p><jats:sec><jats:title>One Sentence Summary</jats:title><jats:p>A machine learning approach to classifying normal and aberrant cell morphology from plate-based imaging of mixed malaria parasite cultures, facilitating clustering of drugs by mechanism of action.</jats:p></jats:sec>
AU  - Ashdown,GW
AU  - Dimon,M
AU  - Fan,M
AU  - Terán,FS-R
AU  - Witmer,K
AU  - Gaboriau,DCA
AU  - Armstrong,Z
AU  - Hazard,J
AU  - Ando,DM
AU  - Baum,J
DO  - 10.1101/2019.12.19.882480
PY  - 2019///
TI  - A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens
UR  - http://dx.doi.org/10.1101/2019.12.19.882480
ER  -
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