Cumulative Learning Enables Convolutional Neural Network Representations for Small Mass Spectrometry Data Classification- Data

Name: Cumulative Learning Enables Convolutional Neural Network Representations for Small Mass Spectrometry Data Classification- Data
Creator: khawla seddiki
Published: 2020-10-21T17:25:18.773Z
Keywords: Mass Spectrometry, Lipidomics, Matrix-Assisted Laser Desorption-Ionization, Imaging Mass Spectrometry

seddiki, khawla; Saudemont, Philippe; Precioso, Frederic; Ogrinc, Nina; Wisztorski, Maxence; salzet, michel; FOURNIER, Isabelle; Droit, Arnoud

doi:10.17632/33cbb37cs2.2

Cumulative Learning Enables Convolutional Neural Network Representations for Small Mass Spectrometry Data Classification- Data

Published: 21-10-2020| Version 2 | DOI: 10.17632/33cbb37cs2.2

Contributors:

khawla seddiki,

Philippe Saudemont,

Frederic Precioso,

Nina Ogrinc,

Maxence Wisztorski,

michel salzet,

Isabelle FOURNIER,

Arnoud Droit

Description

The databanks include raw files from beef liver and microorganisms acquired with the Water-Assisted Laser-Desorption Ionization - SpiderMass technology and a MALDI rat brain tissue image analysed with the Rapiflex (Bruker) at 50 x 50 um resolution in positive ion mode. The SpiderMass is composed of a remote IR laser ablation system and a Synapt G2s from Waters. Each databank consists of raw files, ReadMe file (explaining experimental conditions) and a sample list

Cumulative Learning Enables Convolutional Neural Network Representations for Small Mass Spectrometry Data Classification- Data

Description

Files