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ORIGINAL RESEARCH article

Front. Genet.
Sec. Statistical Genetics and Methodology
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1413484
This article is part of the Research Topic Analysis of multi-omics data and application of artificial intelligence in cancer prevention and treatment View all articles

ScnML models single-cell transcriptome to predict spinal cord neuronal cell status

Provisionally accepted
Lijia Liu Lijia Liu 1Yuxuan Huang Yuxuan Huang 2Zheng Yuan Zheng Yuan 3Yihan Liao Yihan Liao 3Siyuan Ma Siyuan Ma 1Qian Wang Qian Wang 4*
  • 1 Capital Institute of Physical Education and Sports, Beijing, Beijing, China
  • 2 Duke Kunshan University, Kunshan, Jiangsu, China
  • 3 Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, China
  • 4 First Hospital of Tsinghua University, Chaoyang, China

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

    In the manuscript "ScnML models single-cell transcriptome to predict spinal cord neuronal cell status", the authors have integrated scRNA-seq and machine learning to spinal cord nerve cells.The manuscript is well written, and the models performance metrics are impressive. There is no doubt that the tool will be useful in medicine research. I think after fully addressing my comments, this MS will have the merit for contribution in the related area.The full list of 210 marker genes with annotations is required as a supplementary Table .

    Keywords: machine learning, Spinal cord nervous, ScRNA-seq, marker gene, Cell subpopulation

    Received: 07 Apr 2024; Accepted: 20 May 2024.

    Copyright: © 2024 Liu, Huang, Yuan, Liao, Ma and Wang. 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: Qian Wang, First Hospital of Tsinghua University, Chaoyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.