🐟 Fishy Business¶
Machine Learning for Rapid Evaporative Ionization Mass Spectrometry
A Doctoral Thesis by Jesse Wood Victoria University of Wellington
A configuration-driven framework for analyzing mass spectrometry data using Deep Learning, Classic Machine Learning, and Evolutionary Algorithms.
Important
While the source code of this framework is open-source under the MIT license, the accompanying REIMS dataset is private. Authorized users must use the provided download command to fetch the data files.
Quickstart¶
Train a state-of-the-art Transformer model and view results in just 4 lines:
from fishy import TrainingConfig, run_unified_training, display_final_summary
config = TrainingConfig(model="transformer", dataset="species")
results = run_unified_training(config)
display_final_summary(results)
Key Features¶
Universal API: Use the same interface for PyTorch, Scikit-Learn, and DEAP models.
Auto-Validation: Built-in K-Fold cross-validation and statistical significance testing.
Research Ready: Specialized support for pre-training, transfer learning, and contrastive suites.
XAI Integrated: Visual explanations using Grad-CAM and LIME out of the box.
Citation¶
If you use this framework in your research, please cite the following paper:
@article{wood2025hook,
title={Hook, line, and spectra: machine learning for fish species identification and body part classification using rapid evaporative ionization mass spectrometry},
author={Wood, Jesse and Nguyen, Bach and Xue, Bing and Zhang, Mengjie and Killeen, Daniel},
journal={Intelligent Marine Technology and Systems},
volume={3},
number={1},
pages={16},
year={2025},
publisher={Springer}
}
For a full list of related research and publications, see the author’s Google Scholar page.