Comparison Log
2024-05-26 04:47:32.654395
mwtab Python Library Version: 1.2.5
Source:      https://www.metabolomicsworkbench.org/rest/study/analysis_id/AN003329/mwtab/...
Study ID:    ST002045
Analysis ID: AN003329
Status:      Inconsistent

Sections "PROJECT" contain missmatched items: {('PROJECT_SUMMARY', 'The attached MSI datasets of GBM and prostate cancer tissues were analyzed in the manuscript by Abdelmoula et al. (bioRxiv 2021.05.06.442938). The below is taken from the abstract: "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 non-linearity. 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 174-fold gain in speed compared to the established classical machine learning method, support vector machine."'), ('PROJECT_SUMMARY', 'The attached MSI datasets of GBM and prostate cancer tissues were analyzed in the manuscript by Abdelmoula et al. (bioRxiv 2021.05.06.442938). The below is taken from the abstract: 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 non-linearity. 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 174-fold gain in speed compared to the established classical machine learning method, support vector machine.')}
Sections "COLLECTION" contain missmatched items: {('COLLECTION_SUMMARY', 'As stated in the massNetpaper: Briefly, 8 GBM tissue sections of 12 μm thickness were prepared and analyzed using a 9.4 Tesla SolariX mass spectrometer (Bruker Daltonics, Billerica, MA) in the positive ion mode with spatial resolution of 100 μm. The MSI data was exported from SCiLS lab 2020a (Bruker, Bremen, Germany) in the standardized format imzML (Race et al., 2012) and converted to the HDF5 format (Folk et al., 2011) for deep learning analysis.'), ('COLLECTION_SUMMARY', 'As stated in the massNetpaper: "Briefly, 8 GBM tissue sections of 12 μm thickness were prepared and analyzed using a 9.4 Tesla SolariX mass spectrometer (Bruker Daltonics, Billerica, MA) in the positive ion mode with spatial resolution of 100 μm. The MSI data was exported from SCiLS lab 2020a (Bruker, Bremen, Germany) in the standardized format imzML (Race et al., 2012) and converted to the HDF5 format (Folk et al., 2011) for deep learning analysis."')}
Sections "SAMPLEPREP" contain missmatched items: {('SAMPLEPREP_SUMMARY', 'As stated in the massNet paper Briefly, 8 GBM tissue sections of 12 μm thickness were prepared and analyzed using a 9.4 Tesla SolariX mass spectrometer (Bruker Daltonics, Billerica, MA) in the positive ion mode with spatial resolution of 100 μm. The MSI data was exported from SCiLS lab 2020a (Bruker, Bremen, Germany) in the standardized format imzML (Race et al., 2012) and converted to the HDF5 format (Folk et al., 2011) for deep learning analysis.'), ('SAMPLEPREP_SUMMARY', 'As stated in the massNet paper "Briefly, 8 GBM tissue sections of 12 μm thickness were prepared and analyzed using a 9.4 Tesla SolariX mass spectrometer (Bruker Daltonics, Billerica, MA) in the positive ion mode with spatial resolution of 100 μm. The MSI data was exported from SCiLS lab 2020a (Bruker, Bremen, Germany) in the standardized format imzML (Race et al., 2012) and converted to the HDF5 format (Folk et al., 2011) for deep learning analysis."')}
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