Venn diagrams are widely used tools for graphical depiction of the unions, intersections and distinctions among multiple datasets, and a large number of programs have been developed to generate Venn diagrams for applications in various research areas. However, a comprehensive review comparing these tools has not been previously performed. In this review, we collect Venn diagram generators (i.e. tools for visualizing the relationships of input lists within a Venn diagram) and Venn diagram application tools (i.e. tools for analyzing the relationships between biological data and visualizing them in a Venn diagram) to compare their functional capacity as follows: ability to generate high-quality diagrams; maximum datasets handled by each program; input data formats; output diagram styles and image output formats. We also evaluate the picture beautification parameters of the Venn diagram generators in terms of the graphical layout and briefly describe the functional characteristics of the most popular Venn diagram application tools. Finally, we discuss the challenges in improving Venn diagram application tools and provide a perspective on Venn diagram applications in bioinformatics. Our aim is to assist users in selecting suitable tools for analyzing and visualizing user-defined datasets.
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http://dx.doi.org/10.1093/bib/bbab108 | DOI Listing |
Int Urol Nephrol
January 2025
Medical College, Qinghai University, Xining, 810016, People's Republic of China.
Objective: Using machine learning to construct a prediction model for the risk of diabetes kidney disease (DKD) in the American diabetes population and evaluate its effect.
Methods: First, a dataset of five cycles from 2009 to 2018 was obtained from the National Health and Nutrition Examination Survey (NHANES) database, weighted and then standardized (with the study population in the United States), and the data were processed and randomly grouped using R software. Next, variable selection for DKD patients was conducted using Lasso regression, two-way stepwise iterative regression, and random forest methods.
Hereditas
January 2025
Emergency Department, Ningbo Municipal Hospital of Traditional Chinese Medicine, Affiliated Hospital of Zhejiang Chinese Medical University, Ningbo, Zhejiang Province, China.
Endometriosis is a complex gynecological condition characterized by abnormal immune responses. This study aims to explore the immunomodulatory effects of monoterpene glycosides from Paeonia lactiflora on endometriosis. Using the ssGSEA algorithm, we assessed immune cell infiltration levels between normal and endometriosis groups.
View Article and Find Full Text PDFComput Biol Chem
December 2024
Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, Guangdong Pharmaceutical University, Guangzhou 510006, PR China. Electronic address:
In the present study, we uncovered and validated potential biomarkers related to gout, characterized by the accumulation of sodium urate crystals in different joint and non-joint structures. The data set GSE160170 was obtained from the GEO database. We conducted differential gene expression analysis, GO enrichment assessment, and KEGG pathway analysis to understand the underlying processes.
View Article and Find Full Text PDFJ Cancer
January 2025
Department of Oral and Maxillofacial Surgery, School of Stomatology, Hebei Medical University, Hebei Technology Innovation Center of Oral Health, Key Laboratory of Stomatology and Clinical Research Centre for Oral Diseases, Hebei Province, Shijiazhuang, 050017, China.
HOXD13, a member of the homeobox gene family, plays a critical role in developmental processes and has been implicated in various malignancies, including pancreatic cancer and glioma. However, its role in oral squamous cell carcinoma (OSCC) remains poorly understood. This study aimed to elucidate the potential of HOXD13 as a diagnostic biomarker and therapeutic target for OSCC.
View Article and Find Full Text PDFJ Environ Manage
December 2024
Department of Geology, Delhi University(DU), New Delhi, 110007, India.
The study explores the structural and functional dynamics of rhizospheric bacterial diversity in the Pranmati basin, focusing on their ecological significance, diversity, and functional roles across dominant vegetation types; Rhododendron arboreum, Myrica esculenta, and Quercus leucotrichophora. The research provides critical insights into soil health and ecosystem functioning by analysing rhizospheric soil properties among the selected vegetations. The research findings reveal that Myrica esculenta exhibits the highest root colonization (95.
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