ArticleFrontiers in genetics2019
An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction.Computational and structural biotechnology journal · 2025Article
- Three-Dimensional Dense Reconstruction: A Review of Algorithms and Datasets.Sensors (Basel, Switzerland) · 2024Review
- PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation.Scientific reports · 2024Article
- DPI_CDF: druggable protein identifier using cascade deep forest.BMC bioinformatics · 2024Article
- SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks.Biomedicines · 2022Article
- An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network.BMC bioinformatics · 2022Article
- SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information.Frontiers in genetics · 2022Article
- NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information.Evolutionary bioinformatics online · 2020Article
- Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter.BMC genomics · 2019Article
- Article
- MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.Journal of translational medicine · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Self-interacting proteins (SIPs), whose more than two identities can interact with each other, play significant roles in the understanding of cellular process and cell functions. Although a number of experimental methods have been designed to detect the SIPs, they remain to be extremely time-consuming, expensive, and challenging even nowadays. Therefore, there is an urgent need to develop the computational methods for predicting SIPs. In this study, we propose a deep forest based predictor for accurate prediction of SIPs using protein sequence information. More specifically, a novel feature representation method, which integrate position-specific scoring matrix (PSSM) with wavelet transform, is introduced. To evaluate the performance of the proposed method, cross-validation tests are performed on two widely used benchmark datasets. The experimental results show that the proposed model achieved high accuracies of 95.43 and 93.65% on
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