ArticleScientific reports2018
Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions.
Article in Scientific reports, 2018. 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.
- Cracking the black box of deep sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
- An Effective Computational Method for Predicting Self-Interacting Proteins Based on VGGNet Convolutional Neural Network and Gray-Level Co-occurrence Matrix.Evolutionary bioinformatics online · 2024Article
- BJLD-CMI: a predictive circRNA-miRNA interactions model combining multi-angle feature information.Frontiers in genetics · 2024Article
- Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model.Frontiers in microbiology · 2023Article
- A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences.Biology · 2022Article
- RoFDT: Identification of Drug-Target Interactions from Protein Sequence and Drug Molecular Structure Using Rotation Forest.Biology · 2022Article
- MSPEDTI: Prediction of Drug-Target Interactions via Molecular Structure with Protein Evolutionary Information.Biology · 2022Article
- RF_phage virion: Classification of phage virion proteins with a random forest model.Frontiers in genetics · 2022Article
- A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network.Scientific reports · 2021Article
- GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm.PLoS computational biology · 2020Article
- Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions.Scientific reports · 2020Article
- LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities.PLoS computational biology · 2019Article
- BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.International journal of molecular sciences · 2019Article
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Authors and funding
8 authors.
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Abstract
The interaction among proteins is essential in all life activities, and it is the basis of all the metabolic activities of the cells. By studying the protein-protein interactions (PPIs), people can better interpret the function of protein, decoding the phenomenon of life, especially in the design of new drugs with great practical value. Although many high-throughput techniques have been devised for large-scale detection of PPIs, these methods are still expensive and time-consuming. For this reason, there is a much-needed to develop computational methods for predicting PPIs at the entire proteome scale. In this article, we propose a new approach to predict PPIs using Rotation Forest (RF) classifier combine with matrix-based protein sequence. We apply the Position-Specific Scoring Matrix (PSSM), which contains biological evolution information, to represent protein sequences and extract the features through the two-dimensional Principal Component Analysis (2DPCA) algorithm. The descriptors are then sending to the rotation forest classifier for classification. We obtained 97.43% prediction accuracy with 94.92% sensitivity at the precision of 99.93% when the proposed method was applied to the PPIs data of yeast. To evaluate the performance of the proposed method, we compared it with other methods in the same dataset, and validate it on an independent datasets. The results obtained show that the proposed method is an appropriate and promising method for predicting PPIs.
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