Multi-Modal Machine Learning for Population- and Subject-Specific lncRNA-Type 2 Diabetes Association Analysis
For researchers studying lncRNA roles in T2D, this work offers a multi-modal ML approach to identify disease associations, but it is incremental as it applies known methods to a specific domain.
The study developed an integrative multi-feature framework combining expression, secondary-structure, and sequence features of ten lncRNAs, evaluated with eight ML classifiers across two cohorts, identifying GAS5, XIST, MALAT1, and MEG3 as associated with T2D, with MEG3 dominant via SHAP analysis. Results provided population- and subject-level association profiles.
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, MEG3, MIAT, ANRIL, GAS5, KCNQ1OT1, H19, BCYRN1, XIST, and HOTAIR across two independent population-based RNA-seq cohorts. Single-omics approaches provide an incomplete view of disease biology, therefore, an integrative multi-feature framework was developed, extracting expression, secondary-structure, and sequence features for each lncRNA. Eight machine learning (ML) classifiers were evaluated under stratified k-fold, leave-one-out cross-validation (LOOCV), and repeated hold-out schemes to ensure robust performance estimation. SHAP analysis was applied for subject-level association interpretation. In one cohort, GAS5 and XIST expression features, along with GAS5, MEG3, and ANRIL sequence features, were found to be associated with T2D, while MALAT1 expression and KCNQ1OT1, ANRIL, and MEG3 sequence features were found to be associated in the second cohort. MEG3 was identified by SHAP as the dominant lncRNA in both cohorts. ML results were consistent with established statistical methods while additionally providing population- and subject-level disease association profiles linked to specific molecular feature types. The proposed framework advances mechanistic understanding of T2D and supports lncRNA-based precision medicine.