ArticleMolecules (Basel, Switzerland)2017
Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM.
Article in Molecules (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 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
- Recent developments of sequence-based prediction of protein-protein interactions.Biophysical reviews · 2022Review
- Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction.BMC bioinformatics · 2022Article
- A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences.Biology · 2022Article
- An Efficient Computational Model for Large-Scale Prediction of Protein-Protein Interactions Based on Accurate and Scalable Graph Embedding.Frontiers in genetics · 2021Article
- 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
- Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter.BMC genomics · 2019Article
- ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation.Molecular therapy. Nucleic acids · 2019Article
- Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform.International journal of molecular sciences · 2019Article
- Article
- An integration of deep learning with feature embedding for protein-protein interaction prediction.PeerJ · 2019Article
- Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia.BMC bioinformatics · 2018Article
- PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.International journal of molecular sciences · 2018Article
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Authors and funding
5 authors.
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Abstract
Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, computational methods have attracted much attention because of their good performance in detecting PPIs. In our work, a novel computational method named as PCVM-LM is proposed which combines the probabilistic classification vector machine (PCVM) model and Legendre moments (LMs) to predict PPIs from amino acid sequences. The improvement mainly comes from using the LMs to extract discriminatory information embedded in the position-specific scoring matrix (PSSM) combined with the PCVM classifier to implement prediction. The proposed method was evaluated on
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