ArticleBMC bioinformatics2012
Assessing and predicting protein interactions by combining manifold embedding with multiple information integration.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Predicting Protein-Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence.Biology · 2022Article
- SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks.Biomedicines · 2022Article
- Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network.PloS one · 2020Article
- GNE: a deep learning framework for gene network inference by aggregating biological information.BMC systems biology · 2019Article
- Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions.Scientific reports · 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
- CIPPN: computational identification of protein pupylation sites by using neural network.Oncotarget · 2017Article
- 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
- An ensemble approach for large-scale identification of protein- protein interactions using the alignments of multiple sequences.Oncotarget · 2017Article
- Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding.BMC bioinformatics · 2016Article
- Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.BioMed research international · 2016Article
- Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences.BioMed research international · 2016Article
- Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors.International journal of molecular sciences · 2015Article
- Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation.International journal of molecular sciences · 2015Article
- Article
- Protein complex discovery by interaction filtering from protein interaction networks using mutual rank coexpression and sequence similarity.BioMed research international · 2015Article
- Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.BioMed research international · 2014Article
- Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set.BMC bioinformatics · 2014Article
- Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis.BMC bioinformatics · 2013Article
- Introduction: Advanced intelligent computing theories and their applications in bioinformatics.BMC bioinformatics · 2012Article
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5 authors.
Funding
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Abstract
backgroundProtein-protein interactions (PPIs) play crucial roles in virtually every aspect of cellular function within an organism. Over the last decade, the development of novel high-throughput techniques has resulted in enormous amounts of data and provided valuable resources for studying protein interactions. However, these high-throughput protein interaction data are often associated with high false positive and false negative rates. It is therefore highly desirable to develop scalable methods to identify these errors from the computational perspective.
resultsWe have developed a robust computational technique for assessing the reliability of interactions and predicting new interactions by combining manifold embedding with multiple information integration. Validation of the proposed method was performed with extensive experiments on densely-connected and sparse PPI networks of yeast respectively. Results demonstrate that the interactions ranked top by our method have high functional homogeneity and localization coherence.
conclusionsOur proposed method achieves better performances than the existing methods no matter assessing or predicting protein interactions. Furthermore, our method is general enough to work over a variety of PPI networks irrespectively of densely-connected or sparse PPI network. Therefore, the proposed algorithm is a much more promising method to detect both false positive and false negative interactions in PPI networks.
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