Evaluation of the Potential of Deep Learning Methods for Qualitative and Quantitative Analysis of Mass Spectrometry Images, M.Sc. Thesis Sharif University of Technology ; Parastar Shahri, Hadi (Supervisor)
Abstract
In recent years, studying of biological tissues by mass spectrometry imaging (MSI) has been considered due to its selectivity in identifying different compounds in biological tissues, no need for sample preparation, and the possibility of creating the distribution map of these compounds. The complexity of biological tissues due to their heterogeneity, the large volume of data generated, and the effects of competition of other species for ionization in MSI experiments have doubled the importance of using chemometrics to interpret these data. The aim of this work is to quantitatively study Chlordcone as a carcinogenic pesticide and to extract its spatial distribution pattern in mouse liver...
Cataloging briefEvaluation of the Potential of Deep Learning Methods for Qualitative and Quantitative Analysis of Mass Spectrometry Images, M.Sc. Thesis Sharif University of Technology ; Parastar Shahri, Hadi (Supervisor)
Abstract
In recent years, studying of biological tissues by mass spectrometry imaging (MSI) has been considered due to its selectivity in identifying different compounds in biological tissues, no need for sample preparation, and the possibility of creating the distribution map of these compounds. The complexity of biological tissues due to their heterogeneity, the large volume of data generated, and the effects of competition of other species for ionization in MSI experiments have doubled the importance of using chemometrics to interpret these data. The aim of this work is to quantitatively study Chlordcone as a carcinogenic pesticide and to extract its spatial distribution pattern in mouse liver...
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