Fusing Sequence Motifs and Pan-Genomic Features: Antimicrobial Resistance Prediction using an Explainable Lightweight 1D CNN-XGBoost Ensemble

arXiv:2509.23552 · cs.LG, cs.AI, q-bio.GN, q-bio.QM · Submitted 2025-09-28 · Read on arXiv

cs.LG, cs.AI, q-bio.GN, q-bio.QM

Submitted: 2025-09-28

Updated: 2026-09-05

Comments: Published in the proceedings of SCA/HPCAsia 2026. Also available on bioRxiv for visibility to the life sciences community. Published version is available at: https://doi.org/10.1145/3773656.3773682

DOI: 10.1145/3773656.3773682

Code: https://github.com/Saiful185/AMR-EnsembleNet

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

Terminology

• 1D CNN — 22× in this paper · explained in 2 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• F1 Score — 10× in this paper · explained in 3 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Single Nucleotide Polymorphism — 6× in this paper
• Benchmark — 5× in this paper · explained in 2 episodes
• Matthews Correlation Coefficient — 4× in this paper
• Core — 3× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Multi-Layer Perceptron — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Whole Genome Sequencing — 2× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• D Convolutional Neural Network — 1× in this paper
• Ensemble Approach — 1× in this paper · explained in 3 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Independent University Bangladesh — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes

Sources

• Scaling Laws for Neural Language Models
• A Unified Approach to Interpreting Model Predictions

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