Biologically Informed Representation Learning for Robust Cross-Center Generalization of MALDI-TOF Mass Spectrometry

arXiv:2608.08182 · cs.LG, cs.AI · Submitted 2026-08-08 · Read on arXiv

Alejandro L. García-Navarro, Carlos Sevilla-Salcedo, Belén Rodríguez-Sánchez, Vanessa Gómez-Verdejo

cs.LG, cs.AI

Submitted: 2026-08-08

Updated: 2026-08-11

Code: https://github.com/alexgaarciia/MALDIAlign

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Latent Representation — 42× in this paper · explained in 2 episodes
• Decoder — 38× in this paper · explained in 2 episodes
• Representation Learning — 28× in this paper · explained in 3 episodes
• Latent Representations — 24× in this paper · explained in 3 episodes
• Domain Shift — 21× in this paper · explained in 7 episodes
• Latent Space — 14× in this paper · explained in 7 episodes
• Domain Adaptation — 12× in this paper · explained in 5 episodes
• Machine Learning — 9× in this paper
• Benchmark — 8× in this paper · explained in 2 episodes
• Robustness — 8× in this paper · explained in 6 episodes
• Selective Prediction — 8× in this paper · explained in 2 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• AUROC — 5× in this paper · explained in 3 episodes
• Adam — 5× in this paper · explained in 3 episodes
• Generalization — 5× in this paper · explained in 14 episodes
• Hyperparameters — 5× in this paper · explained in 2 episodes
• Transferability — 5× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Distribution Shift — 3× in this paper · explained in 12 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Linear Probe — 3× in this paper · explained in 3 episodes
• Weight Decay — 3× in this paper · explained in 2 episodes
• Ablation Study — 2× in this paper · explained in 3 episodes
• Balanced Accuracy — 2× in this paper
• Benchmarking — 2× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Abstention — 1× in this paper · explained in 1 episode
• Abundance — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 9 episodes
• Class Imbalance — 1× in this paper · explained in 3 episodes
• Cross-Domain Generalization — 1× in this paper · explained in 3 episodes
• Data Heterogeneity — 1× in this paper · explained in 3 episodes
• Disentanglement — 1× in this paper · explained in 2 episodes
• Domain-Adversarial Neural Networks — 1× in this paper
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• False Negatives — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 8 episodes
• Gaussian Mixture Model — 1× in this paper
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• Linear Probing — 1× in this paper · explained in 3 episodes
• Machine Learning (ML) — 1× in this paper · explained in 2 episodes
• Training Objective — 1× in this paper · explained in 3 episodes
• Transformers — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• Zero-shot performance — 1× in this paper · explained in 2 episodes

Sources

• Auto-Encoding Variational Bayes
• Adam: A Method for Stochastic Optimization

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