ArticleInternational journal of molecular sciences2018
SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning.BMC bioinformatics · 2024Article
- Special Protein or RNA Molecules Computational Identification.International journal of molecular sciences · 2023Article
- Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach.ACS omega · 2023Article
- PredictingFrontiers in microbiology · 2023Article
- The Interplay of Oxidative Stress and ROS Scavenging: Antioxidants as a Therapeutic Potential in Sepsis.Vaccines · 2022Review
- HSM6AP: a high-precision predictor for the HomoRNA biology · 2021Article
- Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor.BMC bioinformatics · 2021Article
- Development and validation of a novel predictive model and web calculator for evaluating transfusion risk after spinal fusion for spinal tuberculosis: a retrospective cohort study.BMC musculoskeletal disorders · 2021Article
- sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.Plant molecular biology · 2021Article
- AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs.Frontiers in pharmacology · 2021Review
- DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins.Frontiers in pharmacology · 2021Review
- Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles.Computational and mathematical methods in medicine · 2021Article
- Application of Machine Learning for Drug-Target Interaction Prediction.Frontiers in genetics · 2021Review
- Article
- AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides.Scientific reports · 2020Article
- NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data.Microbial genomics · 2020Article
- Computational Prediction of Protein Arginine Methylation Based on Composition-Transition-Distribution Features.ACS omega · 2020Article
- Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences.Biology · 2020Article
- Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?Molecular therapy. Nucleic acids · 2020Article
- ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features.Frontiers in cell and developmental biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant proteins based on their primary sequence features. The features are removed to reduce the redundancy by max relevance max distance method. Finally, the antioxidant proteins are identified by support vector machine (SVM). The experimental results demonstrated that our method performs better than existing methods, with the overall accuracy of 89.46%. Although a proposed computational method can attain an encouraging classification result, the experimental results are verified based on the biochemical approaches, such as wet biochemistry and molecular biology techniques.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.