Recent advances in neural networks (NN) and machine learning (ML) algorithms have been beneficial in expediting labour-intensive nematode research. In this paper, the authors assessing nematode behaviour, use a high throughput screen called C. elegans Snapshot Analysis Platform (CeSnAP) which enables researchers to go beyond subjective scoring and obtain a more reliable data analysis.
The authors discovered a novel link between branched-chain amino acid transferase 1 (BCAT1) and Parkinson’s Disease (PD) as the RNAi knockdown of neuronal bcat-1 in C. elegans causes abnormal age dependant spasm-like ‘curling’ behaviour. Using CeSnAP, the authors performed high-throughput curling analysis of a total of 17,000 worms in order to identify drugs that ameliorate PD-like motor dysfunction. They found that enasidenib, ethosuximide, metformin, and nitisinone are promising candidates for PD.
This study is an example of the increasing trend towards employing neural networks and machine learning in nematode research and how beneficial it can be to effeciently collect and analyze large amounts of data.
Sohrabi, S., Mor, D. E., Kaletsky, R., Keyes, W., & Murphy, C. T. (2021). High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates. Communications biology, 4(1), 1-9.