ArticleBioMed research international2015
Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Enhancing cross-domain protein and peptide interaction with retrained deep learning models.Briefings in bioinformatics · 2025Article
- Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions.NPJ science of food · 2025Article
- Article
- Cracking the black box of deep sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
- Graph-BERT and language model-based framework for protein-protein interaction identification.Scientific reports · 2023Article
- Assessment of community efforts to advance network-based prediction of protein-protein interactions.Nature communications · 2023Article
- Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model.Frontiers in microbiology · 2023Article
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- RF_phage virion: Classification of phage virion proteins with a random forest model.Frontiers in genetics · 2022Article
- Graph-based prediction of Protein-protein interactions with attributed signed graph embedding.BMC bioinformatics · 2020Article
- FCTP-WSRC: Protein-Protein Interactions PredictionFrontiers in genetics · 2020Article
- Machine-learning techniques for the prediction of protein-protein interactions.Journal of biosciences · 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
- Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest.Scientific reports · 2019Article
- Prediction of Protein-Protein Interactions Based on Domain.Computational and mathematical methods in medicine · 2019Article
- An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation.Frontiers in genetics · 2019Article
- Predicting protein-protein interactions using high-quality non-interacting pairs.BMC bioinformatics · 2018Article
- Accelerating smooth molecular surface calculation.Journal of mathematical biology · 2018Article
- PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.International journal of molecular sciences · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.