MassNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation.

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 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 da

SUBMITTER: Abdelmoula WM 

PROVIDER: S-EPMC8963284 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature