Evidence map›Paper›PMID 38445753›Full record

ArticleBioinformatics (Oxford, England)2024

pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning.

Jonas Cordes, Thomas Enzlein, Carsten Hopf, Ivo Wolf

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jonas CordesFaculty of Computer Science, Mannheim University of Applied Sciences, Mannheim 68163, Germany.ORCID 0000-0003-3148-4282
Thomas EnzleinCenter for Mass Spectrometry and Optical Spectroscopy, Mannheim University of Applied Sciences, Mannheim 68163, Germany.ORCID 0000-0003-1789-4090
Carsten HopfMedical Faculty Mannheim, Heidelberg University, Mannheim 68167, Germany.
Ivo WolfFaculty of Computer Science, Mannheim University of Applied Sciences, Mannheim 68163, Germany.

Funding

German Federal Ministry of Education and ResearchInnovation Partnership M2Aind, project M2Aind-DeepLearning 13FH8I08IA
6 · The paper itself

Abstract

summaryPython is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with a focus on memory-efficient handling, processing and convenient data-access for DL applications. pyM2aia provides interfaces to its parent application M2aia, which offers interactive capabilities for exploring and annotating MSI data in imzML format. pyM2aia utilizes the image input and output routines, data formats, and processing functions of M2aia, ensures data interchangeability, and enables the writing of readable and easy-to-maintain DL pipelines by providing batch generators for typical MSI data access strategies. We showcase the package in several examples, including imzML metadata parsing, signal processing, ion-image generation, and, in particular, DL model training and inference for spectrum-wise approaches, ion-image-based approaches, and approaches that use spectral and spatial information simultaneously. AVAILABILITY AND IMPLEMENTATION: Python package, code and examples are available at (https://m2aia.github.io/m2aia).

Indexed as

Deep LearningSoftwareLanguageMass SpectrometryMetadata

Identifiers

PMID38445753
PMCPMC10948279

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.