AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs.

Yixiao Zhai, Jingyu Zhang, Tianjiao Zhang, Yue Gong, Zixiao Zhang, Dandan Zhang, Yuming Zhao

Abstract readReview

In one paragraph

Review in Frontiers in pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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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.

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3 · Its place in the literature

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1 citing paper in PubMed.

StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning.BMC bioinformatics · 2024
Article

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5 · Who and what money

Authors and funding

7 authors.

Yixiao ZhaiCollege of Information and Computer Engineering, Northeast Forestry University, Harbin, China.

Jingyu ZhangDepartment of Neurology, the Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.

Tianjiao ZhangCollege of Information and Computer Engineering, Northeast Forestry University, Harbin, China.

Yue GongCollege of Information and Computer Engineering, Northeast Forestry University, Harbin, China.

Zixiao ZhangCollege of Information and Computer Engineering, Northeast Forestry University, Harbin, China.

Dandan ZhangDepartment of Obstetrics and Gynecology, the First Affiliated Hospital of Harbin Medical University, Harbin, China.

Yuming ZhaoCollege of Information and Computer Engineering, Northeast Forestry University, Harbin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew's Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

Indexed as

antioxidant drugsantioxidant proteinsdrug screening and discoveryMRMDrandom forest

Identifiers

PMID35115948

DOI10.3389/fphar.2021.818115

PMCPMC8803896

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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.