An efficient framework for protein-protein interaction prediction by integrating stacked denoising autoencoders and random ferns

TL;DR

This paper introduces a new computational method for predicting protein-protein interactions with high accuracy using machine learning techniques.

Contribution

The novel framework combines stacked denoising autoencoders and random ferns for efficient and accurate PPI prediction.

Findings

The model achieved 98.13% and 98.60% accuracy on benchmark datasets.

It demonstrated strong cross-species generalization through independent testing.

The framework is computationally efficient for high-throughput screening.

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.

•Achieves over 98% accuracy using only protein sequence information•Integrates stacked denoising autoencoders and random ferns for robust interaction prediction•Validates strong cross-species generalization capability via independent benchmark testing•Provides a computationally efficient framework suitable for high-throughput screening

Achieves over 98% accuracy using only protein sequence information

Integrates stacked denoising autoencoders and random ferns for robust interaction prediction

Validates strong cross-species generalization capability via independent benchmark testing

Provides a computationally efficient framework suitable for high-throughput screening

Biochemistry; Structural biology; Biocomputational method

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