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m1A-Ensem：通过集成模型准确识别1-甲基腺苷位点。

m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models.

作者信息

Suleman Muhammad Taseer, Alturise Fahad, Alkhalifah Tamim, Khan Yaser Daanial

机构信息

Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan.

Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi Arabia.

出版信息

BioData Min. 2024 Feb 15;17(1):4. doi: 10.1186/s13040-023-00353-x.

DOI:10.1186/s13040-023-00353-x
PMID:38360720
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10868122/
Abstract

BACKGROUND

1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites.

OBJECTIVE

Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated.

METHODOLOGY

The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models.

RESULTS

The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics.

CONCLUSION

For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/ .

摘要

背景

1-甲基腺苷（m1A）是甲基腺苷的一种变体，在第1位含有一个甲基取代基，在RNA稳定性和人类代谢产物中起重要作用。

目的

传统方法，如质谱分析和定点诱变，已被证明既耗时又复杂。

方法

本研究重点是利用新型特征开发机制识别RNA序列中的m1A位点。所获得的特征用于训练集成模型，包括混合、增强和装袋。然后对训练好的集成模型进行独立测试和k折交叉验证。

结果

所提出的模型优于现有的预测器，并根据主要准确性指标显示出优化的分数。

结论

为了研究目的，可以通过https://taseersuleman-m1a-ensem1.streamlit.app/访问所提出模型的用户友好型网络服务器。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/ac6395f055a9/13040_2023_353_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/8dc30371f1fe/13040_2023_353_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/1a800cbf2753/13040_2023_353_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/703b56b3a8d7/13040_2023_353_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/e1a8d3693a04/13040_2023_353_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/6d05f15c7310/13040_2023_353_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/d4dfd6687918/13040_2023_353_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/2ba233d64fb1/13040_2023_353_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/ac6395f055a9/13040_2023_353_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/8dc30371f1fe/13040_2023_353_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/64f65e72c251/13040_2023_353_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/1cd52c30bd1b/13040_2023_353_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/01336e127ee1/13040_2023_353_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/1a800cbf2753/13040_2023_353_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/703b56b3a8d7/13040_2023_353_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/e1a8d3693a04/13040_2023_353_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/6d05f15c7310/13040_2023_353_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/d4dfd6687918/13040_2023_353_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/2ba233d64fb1/13040_2023_353_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b209/10868122/ac6395f055a9/13040_2023_353_Fig11_HTML.jpg

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