Potential risk genes for primary Sjogren's syndrome from a meta-analysis by linear regression and random forest classification.
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ABSTRACT:
SUBMITTER: Cerdo T
PROVIDER: S-EPMC10825437 | biostudies-literature | 2024 May
REPOSITORIES: biostudies-literature
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Potential risk genes for primary Sjogren's syndrome from a meta-analysis by linear regression and random forest classification.
Cerdó Tomás T Torres Moral Teresa T
Genes & diseases 20230704 3
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Project description:We developed Europe-wide models of long-term exposure to eight elements (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in particulate matter with diameter <2.5 μm (PM2.5) using standardized measurements for one-year periods between October 2008 and April 2011 in 19 study areas across Europe, with supervised linear regression (SLR) and random forest (RF) algorithms. Potential predictor variables were obtained from satellites, chemical transport models, land-use, traffic, and industrial point source databases to represent different sources. Overall model performance across Europe was moderate to good for all elements with hold-out-validation R-squared ranging from 0.41 to 0.90. RF consistently outperformed SLR. Models explained within-area variation much less than the overall variation, with similar performance for RF and SLR. Maps proved a useful additional model evaluation tool. Models differed substantially between elements regarding major predictor variables, broadly reflecting known sources. Agreement between the two algorithm predictions was generally high at the overall European level and varied substantially at the national level. Applying the two models in epidemiological studies could lead to different associations with health. If both between- and within-area exposure variability are exploited, RF may be preferred. If only within-area variability is used, both methods should be interpreted equally.
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Project description:PremiseTo improve forest conservation monitoring, we developed a protocol to automatically count and identify the seeds of plant species with minimal resource requirements, making the process more efficient and less dependent on human operators.Methods and resultsSeeds from six North American conifer tree species were separated from leaf litter and imaged on a flatbed scanner. In the most successful species-classification approach, an ImageJ macro automatically extracted measurements for random forest classification in the software R. The method allows for good classification accuracy, and the same process can be used to train the model on other species.ConclusionsThis protocol is an adaptable tool for efficient and consistent identification of seed species or potentially other objects. Automated seed classification is efficient and inexpensive, making it a practical solution that enhances the feasibility of large-scale monitoring projects in conservation biology.
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Project description:The objective of the study was to identify gene expression signatures in minor salivary glands (MSGs) from patients with primary Sjogren's syndrome (SS). METHODS: A 16K complementary DNA microarray was used to generate gene expression profiles in MSGs obtained from 10 patients with primary SS and 10 control subjects. The data were analyzed by 2 different strategies, one strict primary analysis and one subanalysis that allowed for inclusion of genes with no signal in more than 3 samples from each group. The results were validated by quantitative reverse transcriptase-polymerase chain reaction techniques. RESULTS: We found a distinct difference in gene expression levels in MSGs, enabling a simple class prediction method to correctly classify 19 of the 20 samples as either patient or control, based on the top 5 differentially expressed genes. The 50 most differentially expressed genes in the primary SS group compared with the control group were all up-regulated, and a clear pattern of genes involved in chronic inflammation was found. CXCL13 and CD3D were expressed in >/=90% of primary SS patients and in </=10% of the controls. Lymphotoxin beta, as well as a number of major histocompatibility complex genes, cytokines, and lymphocyte activation factors, manifested its role in the pathogenesis of SS. Numerous type I interferon genes related to virus infection were found among the top 200 genes, with increased expression in primary SS. Interestingly, the expression of carbonic anhydrase II, which is essential in saliva production and secretion, and the apoptosis regulator Bcl-2-like 2 were down-regulated in primary SS patients. CONCLUSION: We have identified distinct gene expression profiles in MSGs from patients with primary SS that provide new knowledge about groups of genes that are up-regulated or down-regulated during disease, constituting an excellent platform for forthcoming functional studies.
2013-11-14 | E-MTAB-2073 | biostudies-arrayexpress
Project description:By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.
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Project description:BackgroundThis study evaluates the performance of logistic regression (LR) and random forest (RF) algorithms to model obesity among female adolescents in South Africa.MethodsData was analysed on 375 females aged 15-17 from the South African National Health and Nutrition Examination Survey 2011/2012. The primary outcome was obesity, defined as body mass index (BMI) ≥ 30 kg/m2. A total of 31 explanatory variables were included, ranging from socio-economic, demographic, family history, dietary and health behaviour. RF and LR models were run using imbalanced data as well as after oversampling, undersampling, and hybrid sampling of the data.ResultsUsing the imbalanced data, the RF model performed better with higher precision, recall, F1 score, and balanced accuracy. Balanced accuracy was highest with the hybrid data (0.618 for RF and 0.668 for LR). Using the hybrid balanced data, the RF model performed better (F1-score = 0.940 for RF vs. 0.798 for LR).ConclusionThe model with the highest overall performance metrics was the RF model both before balancing the data and after applying hybrid balancing. Future work would benefit from using larger datasets on adolescent female obesity to assess the robustness of the models.
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