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Machine learning framework links lncRNAs to Type 2 Diabetes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel multi-modal machine learning framework to analyze the association between long non-coding RNAs (lncRNAs) and Type 2 Diabetes (T2D). This approach integrates expression, secondary structure, and sequence features from ten different lncRNAs across two independent cohorts. The framework utilizes eight machine learning classifiers and SHAP analysis to provide population- and subject-specific disease association profiles, advancing the understanding of T2D mechanisms and supporting precision medicine. AI

IMPACT Advances understanding of Type 2 Diabetes mechanisms and supports personalized medicine through novel lncRNA analysis.

RANK_REASON The cluster contains an academic paper detailing a new machine learning methodology for biological analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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Machine learning framework links lncRNAs to Type 2 Diabetes

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The cluster contains an academic paper detailing a new machine learning methodology for biological analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ashwani Siwach, Sanjeev Narayan Sharma, Sunil Datt Sharma ·

    Multi-Modal Machine Learning for Population- and Subject-Specific lncRNA-Type 2 Diabetes Association Analysis

    arXiv:2605.20747v1 Announce Type: cross Abstract: Long non-coding RNAs (lncRNAs) are emerging regulatory molecules implicated in chronic disease pathogenesis, including Type 2 Diabetes Mellitus (T2D). We investigated ten literature reported lncRNAs associated with T2D: MALAT1, ME…

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