Evidence map›Paper›PMID 35237293›Full record

ArticleFrontiers in plant science2022

i6mA-Vote: Cross-Species Identification of DNA N6-Methyladenine Sites in Plant Genomes Based on Ensemble Learning With Voting.

Zhixia Teng, Zhengnan Zhao, Yanjuan Li, Zhen Tian, Maozu Guo, Qianzi Lu, Guohua Wang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 29 citations in OpenAlex.

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  10. Computational prediction of promotors inFrontiers in microbiology · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 1 country.

Zhixia TengCollege of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
Zhengnan ZhaoCollege of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
Yanjuan LiCollege of Electrical and Information Engineering, Quzhou University, Quzhou, China.
Zhen TianCollege of Information Engineering, Zhengzhou University, Zhengzhou, China.
Maozu GuoCollege of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.
Qianzi LuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Guohua WangCollege of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
Northeast Forestry University · CNBeijing University of Civil Engineering and Architecture · CNHarbin Medical University · CNQuzhou University · CNZhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA N6-Methyladenine (6mA) is a common epigenetic modification, which plays some significant roles in the growth and development of plants. It is crucial to identify 6mA sites for elucidating the functions of 6mA. In this article, a novel model named i6mA-vote is developed to predict 6mA sites of plants. Firstly, DNA sequences were coded into six feature vectors with diverse strategies based on density, physicochemical properties, and position of nucleotides, respectively. To find the best coding strategy, the feature vectors were compared on several machine learning classifiers. The results suggested that the position of nucleotides has a significant positive effect on 6mA sites identification. Thus, the dinucleotide one-hot strategy which can describe position characteristics of nucleotides well was employed to extract DNA features in our method. Secondly, DNA sequences of Rosaceae were divided into a training dataset and a test dataset randomly. Finally, i6mA-vote was constructed by combining five different base-classifiers under a majority voting strategy and trained on the Rosaceae training dataset. The i6mA-vote was evaluated on the task of predicting 6mA sites from the genome of the Rosaceae, Rice, and Arabidopsis separately. In Rosaceae, the performances of i6mA-vote were 0.955 on accuracy (ACC), 0.909 on Matthew correlation coefficients (MCC), 0.955 on sensitivity (SN), and 0.954 on specificity (SP). Those indicators, in the order of ACC, MCC, SN, SP, were 0.882, 0.774, 0.961, and 0.803 on Rice while they were 0.798, 0.617, 0.666, and 0.929 on Arabidopsis. According to the indicators, our method was effectiveness and better than other concerned methods. The results also illustrated that i6mA-vote does not only well in 6mA sites prediction of intraspecies but also interspecies plants. Moreover, it can be seen that the specificity is distinctly lower than the sensitivity in Rice while it is just the opposite in Arabidopsis. It may be resulted from sequence similarity among Rosaceae, Rice and Arabidopsis.

Indexed as

cross-speciesensemble learningfeature encodingN6-methyladenineplant genomes

Identifiers

PMID35237293
PMCPMC8882731
OpenAlexW4212885278

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.