A new microarray analyzer based on deep learning

Abstract

Biomarker an indicator that is able to measure metabolism the process of development of disease treatment and at the same time be able to help making clinical decision As it is difficult to finding a biomarker microarray offers a more effective and more convenient way to find biomarkers The E coli proteome microarray one of the microarrays is used as the analysis dataset in this study and it is used to find biomarkers The chip image file produced by the scanner is generally read by microarray image analysis software in this work is GenePix The software automatically detects the signal intensity of each point on the chip and gives the value of the foreground and background of each protein after manual alignment The following analysis algorithms then identify the difference in performance to select proteins with differential expression based on these data However inconsistency between the intensity of the signal given by GenePix and the actual brightness perceived by naked eye is found in this work which results in distortion of the result of selected potential biomarkers In this study we aim to propose a deep-learning-based algorithm to give more accurate gene expressions This deep learning model effectively solves the inconsistency of brightness and the results are verified on the Kawasaki disease dataset we found many candidate proteins that are not only compatible with the naked eye In addition combining candidate proteins can achieve higher efficacy (AUC) base on such algorithm

Date of Award	2020
Original language	English
Supervisor	Tien-Hao Chang (Supervisor)