Back to search

Article

msiPL: Non-linear Manifold and Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks

2020-08-14

Abstract excerpt

Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving clinical diagnosis, biomarker discovery, metabolomics research and pharmaceutical applications. The large data size and high dimensional nature of MSI pose computational and memory complexities that hinder accurate identification of biologically-relevant molecular patterns. We propose msiPL, a robust and generic probabilis...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
14e136f5-8c55-5ace-b8fc-b270e89bc23d
DOI
10.1101/2020.08.13.250142
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
msiPL: Non-linear Manifold and Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural NetworksDOI 10.1101/2020.08.13.250142
Select a neighboring publication to make it the new centre.