Automating the Expert Eye: A System-Agnostic Deep Learning Framework for Rare Event Discovery in Imbalanced Force Spectroscopy

• URL: http://arxiv.org/abs/2606.09541v1
• Date: Mon, 08 Jun 2026 14:24:58 GMT
• Title: Automating the Expert Eye: A System-Agnostic Deep Learning Framework for Rare Event Discovery in Imbalanced Force Spectroscopy
• Abstract summary: We present a system-agnostic, interpretable deep learning framework tailored to overcome extreme class imbalance in automated SMFS triage.<n>We evaluated this framework on the complex mechanical unfolding pathways of the R. champanellensis cellulosome.<n>By implementing an empirically dual-weighted triage system, the pipeline automatically discarded 880 background noise traces.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-Molecule Force Spectroscopy (SMFS) provides unprecedented insights into biomolecular mechanics, yet the high-throughput generation of force-extension trajectories creates a severe data curation bottleneck. Identifying rare molecular unbinding events within thousands of noise-dominated curves traditionally relies on tedious, non-scalable manual auditing. Here, we present a system-agnostic, interpretable deep learning framework tailored to overcome extreme class imbalance in automated SMFS triage. Utilizing 1D-to-2D rasterized geometric matrices, we deployed a modified ResNet18 architecture governed by an asymmetric Focal Loss objective function. We evaluated this framework on the complex mechanical unfolding pathways of the R. champanellensis cellulosome. Under hyper-imbalanced test conditions where the target interaction constituted only 1.34% of the dataset (13 true events out of 970 traces), the model achieved an overall accuracy of 0.9196 and a remarkable True Positive Rate (Recall) of 0.9231. By implementing an empirically calibrated dual-threshold triage system, the pipeline automatically discarded 880 unambiguous background noise traces , reducing the manual curation workload by over 90% while safely preserving high-value rare data. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) visually validated that the network's decisions are firmly anchored in the relevant geometric features of the force curves, specifically localizing on the structural unbinding regions, effectively mitigating 'black-box' skepticism. Built for free cloud-based execution, this open-source tool democratizes scalable, highly precise molecular discovery across the biophysics community.

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