Background: In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required.
Results: We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms.
Conclusions: The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data.
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http://dx.doi.org/10.1186/1755-8794-3-21 | DOI Listing |
JMIR Public Health Surveill
January 2025
School of Public Health, National Defense Medical Center, Taipei City, Taiwan.
Background: Japanese encephalitis (JE) is a zoonotic parasitic disease caused by the Japanese encephalitis virus (JEV), and may cause fever, nausea, headache, or meningitis. It is currently unclear whether the epidemiological characteristics of the JEV have been affected by the extreme climatic conditions that have been observed in recent years.
Objective: This study aimed to examine the epidemiological characteristics, trends, and potential risk factors of JE in Taiwan from 2008 to 2020.
JMIR Med Inform
January 2025
Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Background: Many tools have been developed to predict the risk of diabetes in a population without diabetes; however, these tools have shortcomings that include the omission of race, inclusion of variables that are not readily available to patients, and low sensitivity or specificity.
Objective: We aimed to develop and validate an easy, systematic index for predicting diabetes risk in the Asian population.
Methods: We collected the data from the NAGALA (NAfld [nonalcoholic fatty liver disease] in the Gifu Area, Longitudinal Analysis) database.
Orphanet J Rare Dis
January 2025
Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital, Heinrich- Heine University, Düsseldorf, Germany.
Background: Patients with Gaucher disease (GD) require continual monitoring; however, lack of specific disease biomarkers was a significant challenge in the past. Glucosylsphingosine (lyso-Gb1) has been shown to be a reliable, key, specific, and sensitive biomarker for diagnosis, prognosis, and treatment response in clinical studies of patients with GD. We evaluated the change in lyso-Gb1 concentration over time following enzyme replacement therapy in patients with confirmed GD using real-world data from the Gaucher Outcome Survey disease registry.
View Article and Find Full Text PDFConfl Health
January 2025
London School of Hygiene and Tropical Medicine, Department of Non-Communicable Diseases Epidemiology, Keppel street, London, WC1E 7HT, UK.
Background: Non-communicable diseases (NCDs) are the leading cause of death globally, and many humanitarian crises occur in countries with high NCD burdens. Peer support is a promising approach to improve NCD care in these settings. However, evidence on peer support for people living with NCDs in humanitarian settings is limited.
View Article and Find Full Text PDFNeurol Res Pract
January 2025
Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität Würzburg (JMU), Haus D7, Josef-Schneider-Straße 2, 97080, Würzburg, Germany.
Background: Comprehensive clinical data regarding factors influencing the individual disease course of patients with movement disorders treated with deep brain stimulation might help to better understand disease progression and to develop individualized treatment approaches.
Methods: The clinical core data set was developed by a multidisciplinary working group within the German transregional collaborative research network ReTune. The development followed standardized methodology comprising review of available evidence, a consensus process and performance of the first phase of the study.
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