Metabolomics Navigator Tools, strategies & workflow guidance for metabolomics

Machine learning and prediction

CRISP

CRISP analyzes GC×GC-TOFMS contour images with modules for region selection/deep stacking, synthetic contour generation, image enhancement and classifier training.

GC-MSUntargeted metabolomicsDesktop graphical interfaceSource available with restrictions
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Overview

CRISP analyzes GC×GC-TOFMS contour images with modules for region selection/deep stacking, synthetic contour generation, image enhancement and classifier training. Its graphical research toolkit works on prepared contour representations rather than directly identifying metabolites.

What can this tool do?

Feature selection

Common uses

  • CRISP analyzes GC×GC-TOFMS contour images with modules for region selection/deep stacking, synthetic contour generation, image enhancement and classifier training.

Scientific fit

Primary function

Machine learning and prediction

Other functions

Visualization and reporting

Analysis types

Untargeted metabolomics

Data compatibility

Inputs

Imaging data

Outputs

Imaging data, Statistical results, Human-readable report

Input formats

Prepared GC×GC-TOFMS contour images and module-specific labels/models

Output formats

ROI/deep-stacked contours, generated/enhanced images and model artifacts

Scope and considerations

Designed for

  • CRISP analyzes GC×GC-TOFMS contour images with modules for region selection/deep stacking, synthetic contour generation, image enhancement and classifier training.

Not designed for

  • Molecular identification from raw spectra or evidence that synthetic contours create independent biological samples.

Important requirements

  • The documented deep-learning environment, prepared GC×GC contour images and class labels or model resources appropriate to the selected training/inference module.

Considerations

  • Keep augmentation and evaluation separated to avoid leakage. Contour preprocessing, selected regions and training-domain coverage determine classifier interpretation.

Access and documentation

Primary accessDocumentation

Public research toolkit, but no repository-wide license grant was established. Review dependencies and supplied model/data terms before reuse.

Evidence and review

Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md.

Core publications

Resource papers, direct updates and tool-specific methods. Selection policy.

  1. CRISP: a deep learning architecture for GC × GC–TOFMS contour ROI identification, simulation and analysis in imaging metabolomics. Briefings in Bioinformatics (2022). doi:10.1093/bib/bbab550

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DeepGCN-RTSimilar function · DeepGCN-RT predicts small-molecule retention times from molecular graphs.DeepIONSimilar function · DeepION learns compact representations of MSI ion images for finding co-localized ions or candidate isotope relationships.IDSL_MINTSimilar function · IDSL_MINT provides transformer-based workflows connecting tandem mass spectra and molecular representations.massNetSimilar function · Learn spectral features and classify tissue regions in mass-spectrometry images.

Entry history

Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.

  1. 2026-10-27 · First committed entry

    Add 45 source-reviewed Enveda tool entries in third expansion batch

    View change · Entry at this revision

Full GitHub history