ArticleiScience2026
An efficient framework for protein-protein interaction prediction by integrating stacked denoising autoencoders and random ferns.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Protein-protein interactions (PPIs) are crucial for understanding disease and discovering drug targets. To overcome the limitations of experimental methods, we propose SDAERFs, a computational framework that predicts PPIs from protein sequences. It leverages evolutionary information in position-specific scoring matrices (PSSMs), employs a stacked denoising autoencoder (SDAE) for feature extraction, and uses a Random Ferns (RFs) classifier for prediction. Extensive validation on benchmark datasets yielded high accuracies of 98.13% and 98.60%. Comprehensive comparisons confirmed the model's superior performance. SDAERFs provides an efficient and reliable tool for advancing PPI prediction and therapeutic development.
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