Evidence map›Paper›PMID 40229481›Full record

ArticleCommunications biology2025

Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing.

Xi Hu, Yuwei Shi, Suk Hang Cheng, Zhaoyang Huang, Ze Zhou, Xiaoyu Shi, Yi Zhang, Jing Liu, Mary-Jane L Ma, Spencer C Ding and 10 more

Abstract read
In one paragraph

Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
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

20 authors.

Xi HuCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.
Yuwei ShiCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-0366-5599
Suk Hang ChengCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-2880-2289
Zhaoyang HuangDepartment of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Ze ZhouCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.
Xiaoyu ShiDepartment of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Yi ZhangDepartment of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Jing LiuCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.
Mary-Jane L MaCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.
Spencer C DingCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-7244-5995
Jiaen DengCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.
Rong QiaoCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.
Wenlei PengCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.
L Y Lois ChoyCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-1746-9804
Stephanie C Y YuCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.
W K Jacky LamCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.ORCID http://orcid.org/0000-0001-8922-5609
K C Allen ChanCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.ORCID http://orcid.org/0000-0003-1780-1691
Hongsheng LiDepartment of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Peiyong JiangCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China.
Y M Dennis LoCentre for Novostics, Hong Kong Science Park, Pak Shek Kok, Hong Kong SAR, China. loym@cuhk.edu.hk.ORCID http://orcid.org/0000-0001-8746-0293

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We had previously reported a convolutional neural network (CNN) based approach, called the holistic kinetic model (HK model 1), for detecting 5-methylcytosine (5mC) by single molecule real-time sequencing (Pacific Biosciences). In this study, we constructed a hybrid model with CNN and transformer layers, named HK model 2. We improve the area under the receiver operating characteristic curve (AUC) for 5mC detection from 0.91 for HK model 1 to 0.99 for HK model 2. We further demonstrate that HK model 2 can detect other types of base modifications, such as 5-hydroxymethylcytosine (5hmC) and N6-methyladenine (6mA). Using HK model 2 to analyze 5mC patterns of cell-free DNA (cfDNA) molecules, we demonstrate the enhanced detection of patients with hepatocellular carcinoma, with an AUC of 0.97. Moreover, HK model 2-based detection of 6mA enables the detection of jagged ends of cfDNA and the delineation of cellular chromatin structures. HK model 2 is thus a versatile tool expanding the applications of single molecule real-time sequencing in liquid biopsies.

Indexed as

Carcinoma, HepatocellularDeep LearningDNA MethylationLiver NeoplasmsSequence Analysis, DNA5-MethylcytosineAdenineCell-Free Nucleic AcidsHumansNeural Networks, Computer5-hydroxymethylcytosine5-MethylcytosineAdenineCell-Free Nucleic Acids

Identifiers

PMID40229481
PMCPMC11997116

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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 OpenQuestion graph.