LLM chain-of-thought filtering of Mamba saliency features on TCGA-BRCA data produces a 17-gene set with AUC 0.927 that beats both the raw 50-gene saliency list and a 5000-gene baseline while using far fewer features, though it misses many known BRCA genes.
A review of feature selection methods for machine learning-based disease risk prediction
3 Pith papers cite this work, alongside 814 external citations. Polarity classification is still indexing.
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representative citing papers
A single sentence stating the discovery directly. ≤ 300 chars.
Machine learning pipeline with MICE imputation, tree-based feature selection, and ensemble models predicts birth weight, claiming improved performance on constrained clinical datasets.
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Mamba-SSM with LLM Reasoning for Feature Selection: Faithfulness-Aware Biomarker Discovery
LLM chain-of-thought filtering of Mamba saliency features on TCGA-BRCA data produces a 17-gene set with AUC 0.927 that beats both the raw 50-gene saliency list and a 5000-gene baseline while using far fewer features, though it misses many known BRCA genes.
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Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG
A single sentence stating the discovery directly. ≤ 300 chars.
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Predicting Fetal Birthweight from High Dimensional Data using Advanced Machine Learning
Machine learning pipeline with MICE imputation, tree-based feature selection, and ensemble models predicts birth weight, claiming improved performance on constrained clinical datasets.