Multi-view feature fusion and density-based minority over-sampling technique for amyloid protein prediction under imbalanced data

Abstract

Amyloid fibers formed by the aggregation of amyloid proteins can lead to various neurological diseases. Accurate prediction of amyloid proteins can provide scientific basis for exploring the pathogenesis of related neurological diseases, and developing targeted treatment plans. In view of the limitations of the synthetic minority over-sampling technique (SMOTE), an improved SMOTE method based on density clustering is proposed in this paper, and combined with multi-view features to construct an amyloid protein prediction model. The benefits of this enhanced SMOTE method include eliminating problematic samples, accomplishing an evenly distributed minority sample pool, and utilizing all available boundary sample information. Compared with other over-sampling methods, this method can effectively improve the authenticity and representativeness of the synthesized samples, and has a strong ability to process imbalanced data. After the dataset is over-sampling, different prediction algorithms are trained respectively to construct the baseline models. To fully characterize proteins, the probability and category features are extracted from the baseline models and combined with the sequence features selected by CFS-GSSS (Correlation-based Feature Selection Combined with Greedy Stepwise Search Strategy) to generate the multi-view features. Experimental results demonstrate the complementarity and feasibility of the multi-view features. On the independent test set, compared with the existing best model (ECAmyloid), the proposed amyloid protein prediction model improved the sensitivity and geometric mean by 0.0706 and 0.0129, respectively, far superior to other existing methods.