| 11. |
- Torabi Moghadam, Behrooz, et al.
(författare)
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Analyzing DNA methylation patterns in Schizophrenic patients using machine learning methods
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Schizophrenia is common mental disorder with known genetic component involved. Since the association of environmental factors and schizophrenia has been reported, we analyzed a cohort of 75 schizophrenic and 50 control samples to investigate DNA methylation patterns, as one of the key players of epigenetic gene regulation.Here we applied machine-learning and visualization methods, which were shown previously to be successful in detecting and highlighting differentially methylated patterns between cases and controls. On this data set, however, these methods did not uncover any signal discerning schizophrenia patients and healthy controls, suggesting that if a link exists, it is heterogeneous and complex.
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| 12. |
- Torabi Moghadam, Behrooz, et al.
(författare)
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Combinatorial identification of DNA methylation patterns over age in the human brain
- 2016
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- Background: DNA methylation plays a key role in developmental processes, which is reflected in changing methylation patterns at specific CpG sites over the lifetime of an individual. The underlying mechanisms are complex and possibly affect multiple genes or entire pathways. Results: We applied a multivariate approach to identify combinations of CpG sites that undergo modifications when transitioning between developmental stages. Monte Carlo feature selection produced a list of ranked and statistically significant CpG sites, while rule-based models allowed for identifying particular methylation changes in these sites. Our rule-based classifier reports combinations of CpG sites, together with changes in their methylation status in the form of easy-to-read IF-THEN rules, which allows for identification of the genes associated with the underlying sites. Conclusion: We utilized machine learning and statistical methods to discretize decision class (age) values to get a general pattern of methylation changes over the lifespan. The CpG sites present in the significant rules were annotated to genes involved in brain formation, general development, as well as genes linked to cancer and Alzheimer's disease.
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| 13. |
- Torabi Moghadam, Behrooz
(författare)
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Computational discovery of DNA methylation patterns as biomarkers of ageing, cancer, and mental disorders : Algorithms and Tools
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Epigenetics refers to the mitotically heritable modifications in gene expression without a change in the genetic code. A combination of molecular, chemical and environmental factors constituting the epigenome is involved, together with the genome, in setting up the unique functionality of each cell type.DNA methylation is the most studied epigenetic mark in mammals, where a methyl group is added to the cytosine in a cytosine-phosphate-guanine dinucleotides or a CpG site. It has been shown to have a major role in various biological phenomena such as chromosome X inactivation, regulation of gene expression, cell differentiation, genomic imprinting. Furthermore, aberrant patterns of DNA methylation have been observed in various diseases including cancer.In this thesis, we have utilized machine learning methods and developed new methods and tools to analyze DNA methylation patterns as a biomarker of ageing, cancer subtyping and mental disorders.In Paper I, we introduced a pipeline of Monte Carlo Feature Selection and rule-base modeling using ROSETTA in order to identify combinations of CpG sites that classify samples in different age intervals based on the DNA methylation levels. The combination of genes that showed up to be acting together, motivated us to develop an interactive pathway browser, named PiiL, to check the methylation status of multiple genes in a pathway. The tool enhances detecting differential patterns of DNA methylation and/or gene expression by quickly assessing large data sets.In Paper III, we developed a novel unsupervised clustering method, methylSaguaro, for analyzing various types of cancers, to detect cancer subtypes based on their DNA methylation patterns. Using this method we confirmed the previously reported findings that challenge the histological grouping of the patients, and proposed new subtypes based on DNA methylation patterns. In Paper IV, we investigated the DNA methylation patterns in a cohort of schizophrenic and healthy samples, using all the methods that were introduced and developed in the first three papers.
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| 14. |
- Torabi Moghadam, Behrooz, 1982-, et al.
(författare)
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PiiL : visualization of DNA methylation and gene expression data in gene pathways
- 2017
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- Background: DNA methylation is a major mechanism involved in the epigenetic state of a cell. It has been observed that the methylation status of certain CpG sites close to or within a gene can directly affect its expression, either by silencing or, in some cases, up-regulating transcription. However, a vertebrate genome contains millions of CpG sites, all of which are potential targets for methylation, and the specific effects of most sites have not been characterized to date. To study the complex interplay between methylation status, cellular programs, and the resulting phenotypes, we present PiiL, an interactive gene expression pathway browser, facilitating analyses through an integrated view of methylation and expression on multiple levels.Results: PiiL allows for specific hypothesis testing by quickly assessing pathways or gene networks, where the data is projected onto pathways that can be downloaded directly from the online KEGG database. PiiL provides a comprehensive set of analysis features that allow for quick and specific pattern searches. Individual CpG sites and their impact on host gene expression, as well as the impact on other genes present in the regulatory network, can be examined. To exemplify the power of this approach, we analyzed two types of brain tumors, Glioblastoma multiform and lower grade gliomas.Conclusion: At a glance, we could confirm earlier findings that the predominant methylation and expression patterns separate perfectly by mutations in the IDH genes, rather than by histology. We could also infer the IDH mutation status for samples for which the genotype was not known. By applying different filtering methods, we show that a subset of CpG sites exhibits consistent methylation patterns, and that the status of sites affect the expression of key regulator genes, as well as other genes located downstream in the same pathways.PiiL is implemented in Java with focus on a user-friendly graphical interface. The source code is available under the GPL license from https://github.com/behroozt/PiiL.git.
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| 15. |
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