TY - JOUR
T1 - MassNet
T2 - Integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation
AU - Abdelmoula, Walid M.
AU - Stopka, Sylwia A.
AU - Randall, Elizabeth C.
AU - Regan, Michael
AU - Agar, Jeffrey N.
AU - Sarkaria, Jann N.
AU - Wells, William M.
AU - Kapur, Tina
AU - Agar, Nathalie Y.R.
N1 - Publisher Copyright:
© 2022 The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - Motivation: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. Results: We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine.
AB - Motivation: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. Results: We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine.
UR - http://www.scopus.com/inward/record.url?scp=85128392411&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128392411&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btac032
DO - 10.1093/bioinformatics/btac032
M3 - Article
C2 - 35040929
AN - SCOPUS:85128392411
SN - 1367-4803
VL - 38
SP - 2015
EP - 2021
JO - Bioinformatics
JF - Bioinformatics
IS - 7
ER -