ArticleBMC bioinformatics2018
Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Evaluation of stem/stromal cell transplantation safety and efficacy in children diagnosed with cerebral palsy: A systematic review and meta-analysis of randomized controlled trials.Stem cell research & therapy · 2025Pooled it
- Maternal Inflammation Alters Nuclear and Mitochondrial DNA Methylation Patterns in Neonatal Brain Monocytes.Cells · 2026Article
- Parental kinship influences global methylation and epigenetic age estimation in Peromyscus.Genetics · 2026Article
- Temporally discordant chromatin accessibility and DNA demethylation define short- and long-term enhancer regulation during cell fate specification.Cell reports · 2025Article
- Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study.BMJ paediatrics open · 2024Article
- Review
- Artificial intelligence and machine learning approaches in cerebral palsy diagnosis, prognosis, and management: a comprehensive review.PeerJ. Computer science · 2024Review
- Integrative Multi-Omics Research in Cerebral Palsy: Current Progress and Future Prospects.Neurochemical research · 2023Review
- Towards AI-driven longevity research: An overview.Frontiers in aging · 2023Review
- DNA Methylation Analysis Reveals Distinct Patterns in Satellite Cell-Derived Myogenic Progenitor Cells of Subjects with Spastic Cerebral Palsy.Journal of personalized medicine · 2022Article
- Update on the Molecular Aspects and Methods Underlying the Complex Architecture of FSHD.Cells · 2022Review
- Navigating the pitfalls of applying machine learning in genomics.Nature reviews. Genetics · 2022Review
- An Emerging Role for Epigenetics in Cerebral Palsy.Journal of personalized medicine · 2021Review
- Transcriptional analysis of muscle tissue and isolated satellite cells in spastic cerebral palsy.Developmental medicine and child neurology · 2021Article
- TANC1 methylation as a novel biomarker for the diagnosis of patients with anti-tuberculosis drug-induced liver injury.Scientific reports · 2021Article
- Resistance to Neuromuscular Blockade by Rocuronium in Surgical Patients with Spastic Cerebral Palsy.Journal of personalized medicine · 2021Article
- Clinical epigenomics for cardiovascular disease: Diagnostics and therapies.Journal of molecular and cellular cardiology · 2021Review
- Shared Physiologic Pathways Among Comorbidities for Adults With Cerebral Palsy.Frontiers in neurology · 2021Article
- Skeletal Muscle in Cerebral Palsy: From Belly to Myofibril.Frontiers in neurology · 2021Review
- Meridian acupuncture plus massage for children with spastic cerebral palsy.American journal of translational research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSpastic cerebral palsy (CP) is a leading cause of physical disability. Most people with spastic CP are born with it, but early diagnosis is challenging, and no current biomarker platform readily identifies affected individuals. The aim of this study was to evaluate epigenetic profiles as biomarkers for spastic CP. A novel analysis pipeline was employed to assess DNA methylation patterns between peripheral blood cells of adolescent subjects (14.9 ± 0.3 years old) with spastic CP and controls at single CpG site resolution.
resultsSignificantly hypo- and hyper-methylated CpG sites associated with spastic CP were identified. Nonmetric multidimensional scaling fully discriminated the CP group from the controls. Machine learning based classification modeling indicated a high potential for a diagnostic model, and 252 sets of 40 or fewer CpG sites achieved near-perfect accuracy within our adolescent cohorts. A pilot test on significantly younger subjects (4.0 ± 1.5 years old) identified subjects with 73% accuracy.
conclusionsAdolescent patients with spastic CP can be distinguished from a non-CP cohort based on DNA methylation patterns in peripheral blood cells. A clinical diagnostic test utilizing a panel of CpG sites may be possible using a simulated classification model. A pilot validation test on patients that were more than 10 years younger than the main adolescent cohorts indicated that distinguishing methylation patterns are present earlier in life. This study is the first to report an epigenetic assay capable of distinguishing a CP cohort.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.