ArticleBriefings in bioinformatics2021
DISMIR: Deep learning-based noninvasive cancer detection by integrating DNA sequence and methylation information of individual cell-free DNA reads.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Application of deep learning in cancer epigenetics through DNA methylation analysis.Briefings in bioinformatics · 2023Pooled it
- DNAmBERT: a transformer-based model for non-invasive cancer diagnosis using DNA sequence and methylation data.Briefings in bioinformatics · 2026Article
- UCtracker: A Deep Learning-Based DNA Methylation Model for Noninvasive Diagnosis and Recurrence Surveillance of Urothelial Carcinoma in a Prospective Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Circulating tumor DNA methylation-based method for noninvasive detection and stage stratification of colorectal tumor.Clinical epigenetics · 2026Article
- Genome-wide classification of tumor-derived reads from bulk long-read sequencing.bioRxiv : the preprint server for biology · 2026Article
- Network pharmacology approach to unravel the neuroprotective potential of natural products: a narrative review.Molecular diversity · 2026Review
- Improved circulating tumor DNA identification for detection of esophageal squamous cell carcinoma by enzymatic methyl sequencing and hybrid neural network.Scientific reports · 2025Article
- Genetic Deconvolution of Embryonic and Maternal Cell-Free DNA in Spent Culture Medium of Human Preimplantation Embryo Through Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Deep learning-driven multi-omics analysis: enhancing cancer diagnostics and therapeutics.Briefings in bioinformatics · 2025Review
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data.Genomics, proteomics & bioinformatics · 2025Article
- Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.Genome research · 2025Review
- MethylBERT enables read-level DNA methylation pattern identification and tumour deconvolution using a Transformer-based model.Nature communications · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Liquid biopsy in cancer diagnosis and prognosis: a paradigm shift in precision oncology.Frontiers in molecular biosciences · 2025Review
- Circulating tumor DNA methylation detection as biomarker and its application in tumor liquid biopsy: advances and challenges.MedComm · 2024Review
- Comprehensive review and updated analysis of DNA methylation in hepatocellular carcinoma: From basic research to clinical application.Clinical and translational medicine · 2024Review
- Transformer-based representation learning and multiple-instance learning for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasma cell-free DNA.Molecular oncology · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel noninvasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise predictions with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole-genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as 'switching region' to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultralow sequencing depths. Further analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites' methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.