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

arXiv: https://arxiv.org/abs/2409.02588 · HTML: https://mlsanity.com/paper/2409.02588/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 93 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Random vector functional link network: recent developments, applications, and future directions](https://mlsanity.com/paper/2203.11316/) (2022)
- [CDC: A Simple Framework for Complex Data Clustering](https://mlsanity.com/paper/2403.03670/) (2024)
- [Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning](https://mlsanity.com/paper/2307.07881/) (2023)
- [Deep Learning for Brain Age Estimation: A Systematic Review](https://mlsanity.com/paper/2212.03868/) (2022)
- [A New Learning Paradigm for Random Vector Functional-Link Network: RVFL+](https://mlsanity.com/paper/1708.08282/) (2017)
- [Multiview learning with twin parametric margin SVM](https://mlsanity.com/paper/2408.01981/) (2024)
- [Wave-RVFL: A Randomized Neural Network Based on Wave Loss Function](https://mlsanity.com/paper/2408.02824/) (2024)

## Built on by (most cited first, 8 of 8)

- [GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning](https://mlsanity.com/paper/2409.04743/) (2024)
- [Enhanced Feature Based Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.05786/) (2024)
- [Randomized based restricted kernel machine for hyperspectral image classification](https://mlsanity.com/paper/2503.05837/) (2025)
- [Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning](https://mlsanity.com/paper/2607.05635/) (2026)
- [Graph-Embedded Intuitionistic Fuzzy Broad Learning System: A Multi-view Framework](https://mlsanity.com/paper/2607.16728/) (2026)
- [XGRVFL-MV: Residual-Coupled Graph-Embedded Multi-View Random Vector Functional Link Network with FleXi Guardian Loss](https://mlsanity.com/paper/2607.23149/) (2026)
- [Robust Dual-Model Collaborative Random Vector Functional Link Network](https://mlsanity.com/paper/2608.13628/) (2026)
- [GBFRVFL: Granular-Ball Computing-Based Fuzzy Random Vector Functional Link Network](https://mlsanity.com/paper/2609.29670/) (2026)

## Abstract (arXiv)

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.