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ORIGINAL RESEARCH article

Front. Genet.
Sec. Computational Genomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1498884

PSATF-6mA: An integrated learning fusion feature-encoded DNA-6mA methylcytosine modification site recognition model based on attentional mechanisms

Provisionally accepted
Yanmei Kang Yanmei Kang 1*Hongyuan Wang Hongyuan Wang 1Guanlin Liu Guanlin Liu 1*Yubo Qin Yubo Qin 1*Yi Yu Yi Yu 2*Yongjian Zhang Yongjian Zhang 1*
  • 1 University of International Relations, Beijing, China
  • 2 Guangdong University of Technology, Guangzhou, Guangdong Province, China

The final, formatted version of the article will be published soon.

    DNA methylation is of crucial importance for biological genetic expression, such as biological cell differentiation and cellular tumours. The identification of DNA-6mA sites using traditional biological experimental methods requires more cumbersome steps and a large amount of time. The advent of neural network technology has facilitated the identification of 6mA sites on cross-species DNA with enhanced efficacy. Nevertheless, the majority of contemporary neural network models for identifying 6mA sites prioritize the design of the identification model, with comparatively limited research conducted on the statistically significant DNA sequence itself. Consequently, this paper will focus on the statistical strategy of DNA double-stranded features, utilising the multi-head self-attention mechanism in neural networks applied to DNA position probabilistic relationships. Furthermore, a new recognition model, PSATF-6mA, will be constructed by continually adjusting the attentional tendency of feature fusion through an integrated learning framework. The experimental results, obtained through cross-validation with cross-species data, demonstrate that the PSATF-6mA model outperforms the baseline model. The in-Matthews correlation coefficient (MCC) for the cross-species dataset of rice and m.musus genomes can reach a score of 0.982. The present model is expected to assist biologists in more accurately identifying 6mA locus and in formulating new testable biological hypotheses.

    Keywords: N6-methylcytosine (6mA), Integrated learning, Transfer Learning, Cross -Species, DNA methylation N6-methylcytosine (6mA), DNA Methylation

    Received: 21 Sep 2024; Accepted: 30 Oct 2024.

    Copyright: © 2024 Kang, Wang, Liu, Qin, Yu and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yanmei Kang, University of International Relations, Beijing, China
    Guanlin Liu, University of International Relations, Beijing, China
    Yubo Qin, University of International Relations, Beijing, China
    Yi Yu, Guangdong University of Technology, Guangzhou, 510006, Guangdong Province, China
    Yongjian Zhang, University of International Relations, Beijing, China

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