Development of a machine learning based measurement and statistical modeling of neutrophil extracellular traps by using of convolutional neural networks and semi-supervised learning

Abstract

Summary Neutrophil extracellular traps (NETs) are crucial for the immune response to microorganisms Studies have revealed a close correlation between NETs and autoimmune diseases cancer and aging The use of fluorescence images and DNA fluorescence values to analyze drugs’ inhibition effects on NETs is time-consuming We therefore seek an automated accurate and efficient trap quantification method to resolve this Phorbol myristate acetate was added to neutrophils and further cultured to induce the formation of NETs Dye was added to samples to observe the extracellular DNA of the neutrophils through spectrometry DNA fragments were quantified using a fluorescence reader A convolutional neural network model was formulated and trained using preprocessed data as input data and fluorescence values were applied as labels A combination of machine learning and semi-supervised learning were used to predict the NETs’ DNA fluorescence values and to identify the relationship between fluorescence image input and DNA fluorescence value output for the NETs The method used in this study was confirmed to be feasible with a deviation rate of 3 9923% and a 95% CI of 2 6324%–5 3522% Even with insufficient data semi-supervised learning did not negatively affect the models but rather improved numerical performance Although no statistical significance was achieved semi-supervised learning enabled effective identification of numerous unlabeled clinical data Semi-supervised learning enables unlabeled rare or expensive samples to be effectively used with no negative effects on models The proposed method can replace analysis methods that require numerous samples morphology segmentation and classification Key words: neutrophil extracellular traps NETs machine learning convolutional neural network semi-supervised learning

Date of Award	2021
Original language	English
Supervisor	Chih-Han Chang (Supervisor)