ArticleInternational journal of molecular sciences2017
PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.
Article in International journal of molecular sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed.
- Machine Learning and Deep Learning Frameworks for Human-Virus Protein-Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges.International journal of molecular sciences · 2026Review
- ProteinMAE: masked autoencoder for protein surface self-supervised learning.Bioinformatics (Oxford, England) · 2023Article
- SVSBI: sequence-based virtual screening of biomolecular interactions.Communications biology · 2023Article
- Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.BMC bioinformatics · 2022Article
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction.BMC bioinformatics · 2022Article
- Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation.Computational and mathematical methods in medicine · 2022Article
- Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms.Medical & biological engineering & computing · 2021Article
- Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization.Journal of chemical information and modeling · 2021Article
- An Efficient Computational Model for Large-Scale Prediction of Protein-Protein Interactions Based on Accurate and Scalable Graph Embedding.Frontiers in genetics · 2021Article
- Prediction of Protein-Protein Interactions inFrontiers in genetics · 2021Article
- Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information.Molecules (Basel, Switzerland) · 2020Article
- A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network.BMC medical informatics and decision making · 2020Article
- Protein Interface Complementarity and Gene Duplication Improve Link Prediction of Protein-Protein Interaction Network.Frontiers in genetics · 2020Article
- Using discriminative vector machine model with 2DPCA to predict interactions among proteins.BMC bioinformatics · 2019Article
- Identifying protein complexes based on an edge weight algorithm and core-attachment structure.BMC bioinformatics · 2019Article
- Machine-learning techniques for the prediction of protein-protein interactions.Journal of biosciences · 2019Article
- MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.Journal of translational medicine · 2019Article
- An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model.International journal of molecular sciences · 2019Article
- Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction.International journal of molecular sciences · 2019Article
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6 authors.
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Abstract
Protein-protein interactions (PPIs) are essential for most living organisms' process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of
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