A Robust and Interpretable Feature Engineering Approach for Low-Data Biological Classification
Accurate biological classification often faces challenges from high-dimensional data and limited samples, leading to model overfitting and poor interpretability. This study introduces Directional Flow Embedding (DFE), a novel feature engineering and embedding method designed to overcome these issues. DFE transforms raw biological data into three concise and biologically interpretable features: Directional Flow Score (DFS), Heterogeneity Index (HI), and Local Density Estimate (LDE). It achieves this by robustly determining a global direction vector from class means, enabling the projection of samples onto this principal axis, quantifying their deviation, and incorporating local density information. Evaluated on real-world TCGA-BRCA and TCGA-LUAD RNA-sequencing datasets, DFE consistently demonstrated superior performance. It significantly outperformed traditional methods and strong non-linear models. Ablation studies confirmed the synergistic contribution of all three DFE features, while sensitivity analysis revealed its robustness. DFEs inherent interpretability, strong generalizability, and computational efficiency make it a valuable tool for robust and transparent biological classification, thereby advancing critical applications in biomedical research and clinical decision-making.