Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra

• Published in: BMC bioinformatics Vol. 24; no. 1; pp. 11 - 15
• Main Authors: Mwanga, Emmanuel P., Siria, Doreen J., Mitton, Joshua, Mshani, Issa H., González-Jiménez, Mario, Selvaraj, Prashanth, Wynne, Klaas, Baldini, Francesco, Okumu, Fredros O., Babayan, Simon A.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 09.01.2023; BioMed Central; BMC
• Subjects: Adult; Analysis; Animals; Anopheles; Anopheles arabiensis; Convolutional neural network; Dimensionality reduction; Disease transmission; Female; Generalisability; Humans; Machine Learning; Malaria; Mosquito Vectors; Prevention; Standard machine learning; Transfer learning; South Africa; Dimensionality reduction; Anopheles arabiensis; Generalisability; Transfer learning; Convolutional neural network; Standard machine learning

Old mosquitoes are more likely to transmit malaria than young ones. Therefore, accurate prediction of mosquito population age can drastically improve the evaluation of mosquito-targeted interventions. However, standard methods for age-grading mosquitoes are laborious and costly. We have shown that M...