Multiview Random Vector Functional Link Network for Predicting DNA-Binding Proteins

TL;DR

This paper introduces a multiview neural network model called MvRVFL for predicting DNA-binding proteins, combining multiview learning with neural network architecture to improve prediction accuracy and generalization.

Contribution

The paper proposes a novel multiview neural network framework that fuses features from multiple protein views with a closed-form solution for efficient training and improved prediction of DNA-binding proteins.

Findings

MvRVFL outperforms baseline models on DBP datasets

Model demonstrates superior generalization on benchmark datasets

Theoretical analysis confirms improved learning capabilities

Abstract

The identification of DNA-binding proteins (DBPs) is essential due to their significant impact on various biological activities. Understanding the mechanisms underlying protein-DNA interactions is essential for elucidating various life activities. In recent years, machine learning-based models have been prominently utilized for DBP prediction. In this paper, to predict DBPs, we propose a novel framework termed a multiview random vector functional link (MvRVFL) network, which fuses neural network architecture with multiview learning. The MvRVFL model integrates both late and early fusion advantages, enabling separate regularization parameters for each view, while utilizing a closed-form solution for efficiently determining unknown parameters. The primal objective function incorporates a coupling term aimed at minimizing a composite of errors stemming from all views. From each of the three protein views of the DBP datasets, we extract five features. These features are then fused together by incorporating a hidden feature during the model training process. The performance of the proposed MvRVFL model on the DBP dataset surpasses that of baseline models, demonstrating its superior effectiveness. We further validate the practicality of the proposed model across diverse benchmark datasets, and both theoretical analysis and empirical results consistently demonstrate its superior generalization performance over baseline models.