# Automated Genomic Interpretation via Concept Bottleneck Models for Medical Robotics

arXiv: https://arxiv.org/abs/2510.01618 · HTML: https://mlsanity.com/paper/2510.01618/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Post-hoc Concept Bottleneck Models](https://mlsanity.com/paper/2205.15480/) (2022)
- [FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving](https://mlsanity.com/paper/2505.17685/) (2025)
- [CogVLA: Cognition-Aligned Vision-Language-Action Model via Instruction-Driven Routing & Sparsification](https://mlsanity.com/paper/2508.21046/) (2025)
- [LION-FS: Fast & Slow Video-Language Thinker as Online Video Assistant](https://mlsanity.com/paper/2503.03663/) (2025)
- [Stochastic Concept Bottleneck Models](https://mlsanity.com/paper/2406.19272/) (2024)
- [Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification](https://mlsanity.com/paper/2308.11920/) (2023)
- [Integrating Clinical Knowledge into Concept Bottleneck Models](https://mlsanity.com/paper/2407.06600/) (2024)
- [Vision-Language Embodiment for Monocular Depth Estimation](https://mlsanity.com/paper/2503.16535/) (2025)
- [AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis](https://mlsanity.com/paper/2408.02001/) (2024)
- [Embodiment: Self-Supervised Depth Estimation Based on Camera Models](https://mlsanity.com/paper/2408.01565/) (2024)
- [Adaptive Event Stream Slicing for Open-Vocabulary Event-Based Object Detection via Vision-Language Knowledge Distillation](https://mlsanity.com/paper/2510.00681/) (2025)
- [Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images](https://mlsanity.com/paper/2207.04219/) (2022)
- [A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images](https://mlsanity.com/paper/2307.08308/) (2023)
- [Underground Mapping and Localization Based on Ground-Penetrating Radar](https://mlsanity.com/paper/2409.16446/) (2024)
- [Graph Integrated Multimodal Concept Bottleneck Model](https://mlsanity.com/paper/2510.00701/) (2025)

## Built on by (most cited first, 1 of 1)

- [EmbodiedUS-FS: Fast Slow Intelligence for Ultrasound Robotics](https://mlsanity.com/paper/2606.22319/) (2026)

## Abstract (arXiv)

We propose an automated genomic interpretation module that transforms raw DNA sequences into actionable, interpretable decisions suitable for integration into medical automation and robotic systems. Our framework combines Chaos Game Representation (CGR) with a Concept Bottleneck Model (CBM), enforcing predictions to flow through biologically meaningful concepts such as GC content, CpG density, and k mer motifs. To enhance reliability, we incorporate concept fidelity supervision, prior consistency alignment, KL distribution matching, and uncertainty calibration. Beyond accurate classification of HIV subtypes across both in-house and LANL datasets, our module delivers interpretable evidence that can be directly validated against biological priors. A cost aware recommendation layer further translates predictive outputs into decision policies that balance accuracy, calibration, and clinical utility, reducing unnecessary retests and improving efficiency. Extensive experiments demonstrate that the proposed system achieves state of the art classification performance, superior concept prediction fidelity, and more favorable cost benefit trade-offs compared to existing baselines. By bridging the gap between interpretable genomic modeling and automated decision-making, this work establishes a reliable foundation for robotic and clinical automation in genomic medicine.