ArticleFrontiers in pharmacology2018
Identification of Antioxidant Proteins With Deep Learning From Sequence Information.
Article in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Predicting antioxidant activity of compounds based on chemical structure using machine learning methods.The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2024Article
- Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications.Scientific reports · 2024Article
- ANPrAod: Identify Antioxidant Proteins by Fusing Amino Acid Clustering Strategy andComputational and mathematical methods in medicine · 2021Article
- 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
- Predicting ATP-Binding Cassette Transporters Using the Random Forest Method.Frontiers in genetics · 2020Article
- DeepRTCP: Predicting ATP-Binding Cassette Transporters Based on 1-Dimensional Convolutional Network.Frontiers in cell and developmental biology · 2020Article
- SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome.Molecular therapy. Nucleic acids · 2019Article
- Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics.Frontiers in genetics · 2018Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antioxidant proteins have been found closely linked to disease control for its ability to eliminate excess free radicals. Because of its medicinal value, the study of identifying antioxidant proteins is on the upsurge. Many machine-learning classifiers have performed poorly owing to the nonlinear and unbalanced nature of biological data. Recently, deep learning techniques showed advantages over many state-of-the-art machine learning methods in various fields. In this study, a deep learning based classifier was proposed to identify antioxidant proteins based on mixed g-gap dipeptide composition feature vector. The classifier employed deep autoencoder to extract nonlinear representation from raw input. The t-Distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction. Support vector machine was finally performed for classification. The classifier achieved
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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.