Summary of Multiview Random Vector Functional Link Network For Predicting Dna-binding Proteins, by A. Quadir et al.
Multiview Random Vector Functional Link Network for Predicting DNA-Binding Proteins
by A. Quadir, M. Sajid, M. Tanveer
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel framework, multiview random vector functional link (MvRVFL) network, combines neural networks with multiview learning for accurate DNA-binding protein prediction. The MvRVFL model fuses five features from three protein views, leveraging a closed-form solution to determine unknown parameters efficiently. By incorporating a hidden feature during training and using distinct regularization parameters across views, the model minimizes errors from all views. Experimental results show that the proposed MvRVFL model outperforms baseline models on DBP datasets and generalization capabilities are confirmed through rigorous statistical analyses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to predict DNA-binding proteins (DBPs) using machine learning. By combining different types of data, called “views”, the model can learn to identify DBPs more accurately than other methods. The model uses a special type of neural network and takes into account different parts of the protein when making its predictions. This approach is tested on several datasets and shows that it works better than previous methods. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Neural network » Regularization
