ArticleInternational journal of molecular sciences2018
PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Special Protein or RNA Molecules Computational Identification.International journal of molecular sciences · 2023Article
- Computational Modeling, High-Level Soluble Expression and In Vitro Cytotoxicity Assessment of RecombinantPharmaceutics · 2023Article
- Molecular fusion events in carcinogenic organisms: a bioinformatics study for the detection of fused proteins between viruses, bacteria and eukaryotes.EMBnet.journal · 2022Article
- Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation.Computational and mathematical methods in medicine · 2022Article
- Prediction of the Effects of Variants and Differential Expression of Key Host GenesBioinformatics and biology insights · 2021Article
- Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction.International journal of molecular sciences · 2019Article
- BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.International journal of molecular sciences · 2019Article
- Handling Noise in Protein Interaction Networks.BioMed research international · 2019Article
- Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer-Based Relevance Vector Machine.Evolutionary bioinformatics online · 2019Article
- An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation.Frontiers in genetics · 2019Article
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Authors and funding
5 authors.
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Abstract
Protein-protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular biology. As researchers have become aware of the importance of computational methods in predicting PPIs, many techniques have been developed for performing this task computationally. However, there are few technologies that really meet the needs of their users. In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. In order to verify the performance of our method, five-fold cross-validation tests are performed on the
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Registered trials
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