Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy

Crowgey, Erin L.; Marsh, Adam G.; Robinson, Karyn G.; Yeager, Stephanie Kim; Akins, Robert E.

doi:10.1186/s12859-018-2224-0

Cited by 44 publications

(50 citation statements)

References 45 publications

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“…Thus, our results and previously published data suggest that a treatment such as 5-AZA targeting a single epigenetic event (DNA methylation) is likely not a successful therapy on a large scale in a clinical setting. For example, in one of the aforementioned studies, among the top 200 differentially methylated genes in white blood cells in CP, roughly half were hypomethylated as compared to TD children (30). With this in mind, a clear and noteworthy difference between the aforementioned studies and our investigation is the level of specificity, related to both the target gene (rDNA) and the target organ (skeletal muscle rather than global methylation of nucleated blood cells).…”

Section: Discussionmentioning

confidence: 80%

“…However, speaking against a general inability to methylate DNA, as suggested by Schoendorfer et al is a series of papers published in recent years that investigate differences in global DNA methylation between CP and TD children, i.e., DNA methylation in white blood cells. Two of these studies are case (CP) vs. control (TD) studies (30,31), and two are studies on monozygotic twins discordant for CP (32,33). All of these studies indicate that distinct epigenetic imprinting is evident in CP and that this is detectable very early on, even before 1 year of age.…”

Section: Discussionmentioning

confidence: 99%

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Epigenetic Marks at the Ribosomal DNA Promoter in Skeletal Muscle Are Negatively Associated With Degree of Impairment in Cerebral Palsy

et al. 2020

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Introduction: Cerebral palsy (CP) is the most common motor impairment in children. Skeletal muscles in individuals with CP are typically weak, thin, and stiff. Whether epigenetic changes at the ribosomal DNA (rDNA) promoter are involved in this dysregulation remains unknown. Methods: Skeletal muscle samples were collected from 19 children with CP and 10 typically developed (TD) control children. Methylation of the rDNA promoter was analyzed using the Agena Epityper Mass array and gene expression by qRT-PCR. Results: Biceps brachii muscle ribosome biogenesis was suppressed in CP as compared to TD. Average methylation of the rDNA promoter was not different between CP and TD but negatively correlated to elbow flexor contracture in the CP group. Discussions: We observed a negative correlation between rDNA promoter methylation and degree of muscle contracture in the CP group. Children with CP with more severe motor impairment had less methylation of the rDNA promoter compared to less affected children. This finding suggests the importance of neural input and voluntary muscle movements for promoter methylation to occur in the biceps muscle.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Epigenetic Marks at the Ribosomal DNA Promoter in Skeletal Muscle Are Negatively Associated With Degree of Impairment in Cerebral Palsy

et al. 2020

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“…Further, in epigenetics many new approaches for machine learning are proposed and used in direct application [20,21]. We will need data for machine learning which hold the assumptions to the data.…”

Section: Discussionmentioning

confidence: 99%

Detection of suspicious interactions of spiking covariates in methylation data

et al. 2020

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Background: In methylation analyses like epigenome-wide association studies, a high amount of biomarkers is tested for an association between the measured continuous outcome and different covariates. In the case of a continuous covariate like smoking pack years (SPY), a measure of lifetime exposure to tobacco toxins, a spike at zero can occur. Hence, all non-smokers are generating a peak at zero, while the smoking patients are distributed over the other SPY values. Additionally, the spike might also occur on the right side of the covariate distribution, if a category "heavy smoker" is designed. Here, we will focus on methylation data with a spike at the left or the right of the distribution of a continuous covariate. After the methylation data is generated, analysis is usually performed by preprocessing, quality control, and determination of differentially methylated sites, often performed in pipeline fashion. Hence, the data is processed in a string of methods, which are available in one software package. The pipelines can distinguish between categorical covariates, i.e. for group comparisons or continuous covariates, i.e. for linear regression. The differential methylation analysis is often done internally by a linear regression without checking its inherent assumptions. A spike in the continuous covariate is ignored and can cause biased results. Results: We have reanalysed five data sets, four freely available from ArrayExpress, including methylation data and smoking habits reported by smoking pack years. Therefore, we generated an algorithm to check for the occurrences of suspicious interactions between the values associated with the spike position and the non-spike positions of the covariate. Our algorithm helps to decide if a suspicious interaction can be found and further investigations should be carried out. This is mostly important, because the information on the differentially methylated sites will be used for post-hoc analyses like pathway analyses. Conclusions: We help to check for the validation of the linear regression assumptions in a methylation analysis pipeline. These assumptions should also be considered for machine learning approaches. In addition, we are able to detect outliers in the continuous covariate. Therefore, more statistical robust results should be produced in methylation analysis using our algorithm as a preprocessing step.

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“…To date, ML has yielded limited biomarkers that have made it into current clinical practice. However, it is likely that in the upcoming decades the application of ML to the epigenome [38] will yield many more potential biomarkers and drug targets, particularly because ML is optimized to find meaning in large and complex data sets. In genomics and transcriptomics, ML methods are already used for example in gene set enrichment analysis, to find highly overrepresented pathways [39].…”

Section: Epigenetics and Its Clinical Potentialmentioning

confidence: 99%

“…example, between breast cancer brain metastases subtypes [38,57]. Unsupervised learning algorithms are especially useful to detect patterns in data sets that have large amounts of data points, such as those in microarray and omics data sets [66,67].…”

Section: Area Under the Receiver Operator Curve (Roc Auc)mentioning

confidence: 99%

Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification

et al. 2020

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Background: Machine learning is a sub-field of artificial intelligence, which utilises large data sets to make predictions for future events. Although most algorithms used in machine learning were developed as far back as the 1950s, the advent of big data in combination with dramatically increased computing power has spurred renewed interest in this technology over the last two decades. Main body: Within the medical field, machine learning is promising in the development of assistive clinical tools for detection of e.g. cancers and prediction of disease. Recent advances in deep learning technologies, a subdiscipline of machine learning that requires less user input but more data and processing power, has provided even greater promise in assisting physicians to achieve accurate diagnoses. Within the fields of genetics and its sub-field epigenetics, both prime examples of complex data, machine learning methods are on the rise, as the field of personalised medicine is aiming for treatment of the individual based on their genetic and epigenetic profiles. Conclusion: We now have an ever-growing number of reported epigenetic alterations in disease, and this offers a chance to increase sensitivity and specificity of future diagnostics and therapies. Currently, there are limited studies using machine learning applied to epigenetics. They pertain to a wide variety of disease states and have used mostly supervised machine learning methods. Background Clinical epigenetics is a promising field of research. There is evidence that DNA methylation changes at cytosinephosphate-guanine (CpG) sites are associated with disease development [1-3]. Beyond genetic background, DNA methylation may additionally reflect environmental exposures and could improve diagnostic accuracy and prognostic prediction of certain diseases and be targetable by personalised therapy in the future [4, 5]. The current medical environment is characterised by collection of vast amounts of patient, hospital, and administrative data [6, 7], which makes traditional approaches to investigating these data individually less ideal. Machine learning (ML), however, is able to integrate large and complex data sets [8]. These data sources have the potential to enhance patient care and outcomes. A personalised medicine approach is tightly connected to increases in omics-data. For example, DNA sequence databases double in size twice a year [9]. Indeed, the increases in computer processing coupled with the rapid reduction in the cost of genomic sequencing have outpaced the rate of computing hardware advances [10]. Whilst far from a panacea, ML may be a tool to assist physicians in interpreting information-rich clinical data, including those collected in epigenetic studies [11, 12].

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Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy

Cited by 44 publications

References 45 publications

Epigenetic Marks at the Ribosomal DNA Promoter in Skeletal Muscle Are Negatively Associated With Degree of Impairment in Cerebral Palsy

Epigenetic Marks at the Ribosomal DNA Promoter in Skeletal Muscle Are Negatively Associated With Degree of Impairment in Cerebral Palsy

Detection of suspicious interactions of spiking covariates in methylation data

Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification

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