Mamba-SSM with LLM Reasoning for Biomarker Discovery: Causal Feature Refinement via Chain-of-Thought Gene Evaluation
Pushpa Kumar Balan, Aijing Feng
TLDR
Mamba-SSM and LLM CoT refine biomarker discovery, improving classification performance with fewer features, even with selective reasoning faithfulness.
Key contributions
- Mamba-SSM identifies candidate biomarkers from RNA-seq data using gradient saliency.
- LLM Chain-of-Thought reasoning filters initial gene lists, removing confounders for improved performance.
- LLM-filtered 17-gene set outperforms a 5,000-gene baseline (AUC 0.927) using 294x fewer features.
- Introduces 'selective faithfulness,' where targeted confounder removal boosts performance despite incomplete recall.
Why it matters
This research introduces a novel method combining deep sequence models with LLM reasoning for biomarker discovery. It significantly improves classification accuracy while drastically reducing feature count. The concept of 'selective faithfulness' offers a new perspective on LLM utility in scientific discovery.
Original Abstract
Gradient saliency from deep sequence models surfaces candidate biomarkers efficiently, but the resulting gene lists are contaminated by tissue-composition confounders that degrade downstream classifiers. We study whether LLM chain-of-thought (CoT) reasoning can faithfully filter these confounders, and whether reasoning quality drives downstream performance. We train a Mamba SSM on TCGA-BRCA RNA-seq and extract the top-50 genes by gradient saliency; DeepSeek-R1 evaluates every candidate with structured CoT to produce a final 17-gene set. The raw 50-gene saliency set (no LLM) performs worse than a 5,000-gene variance baseline (AUC 0.832 vs. 0.903), while the LLM-filtered set surpasses it (AUC 0.927), using 294x fewer features. A faithfulness audit (COSMIC CGC, OncoKB, PAM50) reveals only 6 of 17 selected genes (35.3%) are validated BRCA biomarkers, yet 10 of 16 known BRCA genes in the input were missed - including FOXA1. This gap between downstream performance and reasoning faithfulness suggests selective faithfulness: targeted confounder removal is sufficient for performance gains even without comprehensive recall.
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