| 1. |
- Georgiadis, Panagiotis, et al.
(författare)
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Omics for prediction of environmental health effects : Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking.
- 2016
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.
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| 2. |
- Logotheti, Marianthi, 1986-, et al.
(författare)
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Development and validation of a skin fibroblast biomarker profile for schizophrenic patients
- 2016
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Ingår i: AIMS Bioengineering. - Springfield, USA : AIMS press. - 2375-1487 .- 2375-1495. ; 3:4, s. 552-565
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Tidskriftsartikel (refereegranskat)abstract
- Gene expression profiles of non-neural tissues through microarray technology could be used in schizophrenia studies, adding more information to the results from similar studies on postmortem brain tissue. The ultimate goal of such studies is to develop accessible biomarkers. Supervised machine learning methodologies were used, in order to examine if the gene expression from skin fibroblast cells could be exploited for the classification of schizophrenic subjects. A dataset of skin fibroblasts gene expression of schizophrenia patients was obtained from Gene Expression Omnibus database. After applying statistical criteria, we concluded to genes that present a differential expression between the schizophrenic patients and the healthy controls. Based on those genes, functional profiling was performed with the BioInfoMiner web tool. After the statistical analysis, 63 genes were identified as differentially expressed. The functional profiling revealed interesting terms and pathways, such as mitogen activated protein kinase and cyclic adenosine monophosphate signaling pathways, as well as immune-related mechanisms. A subset of 16 differentially expressed genes from fibroblast gene expression profiling that occurred after Support Vector Machines Recursive Feature Elimination could efficiently separate schizophrenic from healthy controls subjects. These findings suggest that through the analysis of fibroblast based gene 553 expression signature and with the application of machine learning methodologies we might conclude to a diagnostic classification model in schizophrenia.
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| 3. |
- Logotheti, Marianthi, 1986-, et al.
(författare)
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Studying Microarray Gene Expression Data of Schizophrenic Patients for Derivation of a Diagnostic Signature through the Aid of Machine Learning
- 2016
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Ingår i: Biometrics & Biostatistics International Journal. - : MedCrave. - 2378-315X. ; 4:5
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Tidskriftsartikel (refereegranskat)abstract
- Schizophrenia is a complex psychiatric disease that is affected by multiple genes, some of which could be used as biomarkers for specific diagnosis of the disease. In this work, we explore the power of machine learning methodologies for predicting schizophrenia, through the derivation of a biomarker gene signature for robust diagnostic classification purposes. Postmortem brain gene expression data from the anterior prefrontal cortex of schizophrenia patients were used as training data for the construction of the classifiers. Several machine learning algorithms, such as support vector machines, random forests, and extremely randomized trees classifiers were developed and their performance was tested. After applying the feature selection method of support vector machines recursive feature elimination a 21-gene model was derived. Using these genes for developing classification models, the random forests algorithm outperformed all examined algorithms achieving an area under the curve of 0.98 and sensitivity of 0.89, discriminating schizophrenia from healthy control samples with high efficiency. The 21-gene model that was derived from the feature selection is suggested for classifying schizophrenic patients, as it was successfully applied on an independent dataset of postmortem brain samples from the superior temporal cortex, and resulted in a classification model that achieved an area under the curve score of 0.91. Additionally, the functional analysis of the statistically significant genes indicated many mechanisms related to the immune system.
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