Summary of project PR001292
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001292. The data can be accessed directly via it's Project DOI: 10.21228/M8Q70T This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Project ID: | PR001292 |
| Project DOI: | doi: 10.21228/M8Q70T |
| Project Title: | massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation |
| Project Type: | integrated processing and classification of MSI data analysis |
| 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. |
| Institute: | Brigham and Women's Hospital |
| Department: | Department of Neurosurgery |
| Laboratory: | Nathalie Y.R. Agar |
| Last Name: | Abdelmoula |
| First Name: | Walid |
| Address: | 60 Fenwood RD, Boston, MA |
| Email: | wahassan@bwh.harvard.edu |
| Phone: | 8572149765 |
Summary of all studies in project PR001292
| Study ID | Study Title | Species | Institute | Analysis(* : Contains Untargted data) | Release Date | Version | Samples | Download(* : Contains raw data) |
|---|---|---|---|---|---|---|---|---|
| ST002045 | massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation | Mus musculus | Brigham and Women's Hospital | MS* | 2022-01-04 | 1 | 8 | Uploaded data (523.6M)* |
