ArticleFrontiers in cell and developmental biology2020
Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions.
Article in Frontiers in cell and developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 52 citations in OpenAlex.
- A First Computational Frame for Recognizing Heparin-Binding Protein.Diagnostics (Basel, Switzerland) · 2023Article
- IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.Computational intelligence and neuroscience · 2023Article
- Bitter-RF: A random forest machine model for recognizing bitter peptides.Frontiers in medicine · 2023Article
- Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods.Computational and structural biotechnology journal · 2023Review
- iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraints and global topological constraints.RNA (New York, N.Y.) · 2022Article
- Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM.Computational and mathematical methods in medicine · 2022Article
- MoRF-FUNCpred: Molecular Recognition Feature Function Prediction Based on Multi-Label Learning and Ensemble Learning.Frontiers in pharmacology · 2022Review
- KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest.Frontiers in genetics · 2021Article
- Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D.Frontiers in genetics · 2021Article
- Research on the Computational Prediction of Essential Genes.Frontiers in cell and developmental biology · 2021Review
- Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies.Frontiers in genetics · 2021Article
- SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles.Frontiers in genetics · 2021Article
- Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases.Frontiers in genetics · 2021Review
- AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs.Frontiers in pharmacology · 2021Review
- Immunoglobulin Classification Based on FC* and GC* Features.Frontiers in genetics · 2021Article
- A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem.Frontiers in genetics · 2021Article
- Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm.Frontiers in genetics · 2021Review
- Identification of Prognostic Biomarkers for Bladder Cancer Based on DNA Methylation Profile.Frontiers in cell and developmental biology · 2021Article
- VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost.Frontiers in genetics · 2021Article
- Application of Multilayer Network Models in Bioinformatics.Frontiers in genetics · 2021Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Excessive oxidative stress responses can threaten our health, and thus it is essential to produce antioxidant proteins to regulate the body's oxidative responses. The low number of antioxidant proteins makes it difficult to extract their representative features. Our experimental method did not use structural information but instead studied antioxidant proteins from a sequenced perspective while focusing on the impact of data imbalance on sensitivity, thus greatly improving the model's sensitivity for antioxidant protein recognition. We developed a method based on the Composition of k-spaced Amino Acid Pairs (CKSAAP) and the Conjoint Triad (CT) features derived from the amino acid composition and protein-protein interactions. SMOTE and the Max-Relevance-Max-Distance algorithm (MRMD) were utilized to unbalance the training data and select the optimal feature subset, respectively. The test set used 10-fold crossing validation and a random forest algorithm for classification according to the selected feature subset. The sensitivity was 0.792, the specificity was 0.808, and the average accuracy was 0.8.
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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.