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Background Set comparisons permeate a large number of data analysis workflows, in particular workflows in biological sciences. Venn diagrams are frequently employed for such analysis but current tools are limited. Results We have developed InteractiVenn, a more flexible tool for interacting with Venn diagrams including up to six sets. It offers a clean interface for Venn diagram construction and enables analysis of set unions while preserving the shape of the diagram. Set unions are useful to reveal differences and similarities among sets and may be guided in our tool by a tree or by a list of set unions. The tool also allows obtaining subsets’ elements, saving and loading sets for further analyses, and exporting the diagram in vector and image formats. InteractiVenn has been used to analyze two biological datasets, but it may serve set analysis in a broad range of domains. Conclusions InteractiVenn allows set unions in Venn diagrams to be explored thoroughly, by consequence extending the ability to analyze combinations of sets with additional observations, yielded by novel interactions between joined sets. InteractiVenn is freely available online at: www.interactivenn.net.

Venn diagrams serve as invaluable tools for visualizing set relationships due to their ease of interpretation. Widely applied across diverse disciplines such as metabolomics, genomics, transcriptomics, and proteomics, their utility is undeniable. However, the operational complexity has been compounded by the absence of standardized data formats and the need to switch between various platforms for generating different Venn diagrams. To address these challenges, we introduce the EVenn platform, a versatile tool offering a unified interface for efficient data exploration and visualization of diverse Venn diagrams. EVenn (http://www.ehbio.com/test/venn) streamlines the data upload process with a standardized format, enhancing the capabilities for multimodule analysis. This comprehensive protocol outlines various applications of EVenn, featuring representative results of multiple Venn diagrams, data uploads in the centralized data center, and step‐by‐step case demonstrations. Through these functionalities, EVenn emerges as a valuable and user‐friendly tool for the in‐depth exploration of multiomics data. The use of Venn diagrams greatly aids in illustrating and visualizing set relationships within metabolomics, genomics, transcriptomics, and proteomics. Here, we introduce EVenn, an all‐encompassing platform that provides a unified interface for a wide range of Venn diagram functionalities. This protocol delineates the features of EVenn, encompassing interactive Venn diagrams, interactive Edwards diagrams, Euler diagrams, UpSet plots, flower plots, and Venn network diagrams, solidifying its role as an invaluable tool for the analysis of multiomics data. Highlights Comprehensive Venn functionality: EVenn introduces a unified platform with diverse Venn diagram tools, from interactive diagrams to network representations, catering to the intricate needs of multiomics data analysis across metabolomics, genomics, transcriptomics, and proteomics. Efficient data exploration: EVenn's data center streamlines exploration by supporting a standardized data format, allowing researchers to effortlessly upload and analyze data. The manuscript demonstrates its practical utility through representative results and detailed case demonstrations. User‐friendly interface: With a user‐friendly interface, EVenn simplifies the generation of various Venn diagrams, Euler diagrams, UpSet plots, and more. This protocol establishes EVenn as a valuable resource for researchers seeking a cohesive and accessible tool for multiomics data interpretation.

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis.

Background The main research topic in this paper is how to compare multiple biological experiments using transcriptome data, where each experiment is measured and designed to compare control and treated samples. Comparison of multiple biological experiments is usually performed in terms of the number of DEGs in an arbitrary combination of biological experiments. This process is usually facilitated with Venn diagram but there are several issues when Venn diagram is used to compare and analyze multiple experiments in terms of DEGs. First, current Venn diagram tools do not provide systematic analysis to prioritize genes. Because that current tools generally do not fully focus to prioritize genes, genes that are located in the segments in the Venn diagram (especially, intersection) is usually difficult to rank. Second, elucidating the phenotypic difference only with the lists of DEGs and expression values is challenging when the experimental designs have the combination of treatments. Experiment designs that aim to find the synergistic effect of the combination of treatments are very difficult to find without an informative system. Results We introduce Venn-diaNet, a Venn diagram based analysis framework that uses network propagation upon protein-protein interaction network to prioritizes genes from experiments that have multiple DEG lists. We suggest that the two issues can be effectively handled by ranking or prioritizing genes with segments of a Venn diagram. The user can easily compare multiple DEG lists with gene rankings, which is easy to understand and also can be coupled with additional analysis for their purposes. Our system provides a web-based interface to select seed genes in any of areas in a Venn diagram and then perform network propagation analysis to measure the influence of the selected seed genes in terms of ranked list of DEGs. Conclusions We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianet

At the initial stages of studying the theory of Boolean algebras, before trying to prove or disprove any simple sentence, students are usually asked to test their intuition using Venn diagrams or truth tables. A natural question arises: is it necessary to invent a proof after a positive check of this kind? Isn’t such a check itself a rigorous proof of the verified sentence? And if this is not true in the general case, for which sentences is this true? We answer the question and prove an analog of the Jech Theorem for arbitrary (not necessarily complete) Boolean algebras.

At different time scales, a species will experience diverse distribution changes. For Epimedium brevicornum Maxim, the phenomenon is obvious, but the understanding of the spatial dynamics of E. brevicornum under distinct time scales is poor. In this study, we modeled the potential distribution for E. brevicornum for five time scales, 1970–1979, 1980–1989, 1990–1999, 2000–2009, and 2010–2019, with different occurrence data, and the Kuenm package was used to optimize the parameter combination. Then, SDM tools and a Venn diagram were utilized to simulate the changes in highly suitable areas and spatial dynamics, respectively. Comprehensive results show that temperature seasonality (BIO4, 37.54%) has the greatest effect on the distribution of E. brevicornum, followed by minimum temperature (TMIN, 21.42%). The areas of distribution for E. brevicornum are 35.06 × 105 km2, 25.7 × 105 km2, 67.64 × 105 km2, 27.29 × 105 km2, and 9.87× 105 km2, which are mainly concentrated in Gansu, Shaanxi, Shanxi, and Henan, respectively. In addition, the largest regions for expansion, stability, and contraction under various time scales are 5.6 × 105 km2, 3.54 × 105 km2, and 3.47 × 105 km2, respectively. These changes indicate that approximately 7.96% of the regions are highly stable, and three critical counties, Wanyuan, Chenggu, and Hechuan, and Xixiang, have become significant areas for migration. Overall, our results indicate that there are different spatial distribution patterns and dynamics for E. brevicornum for different time scales. Given these results, this study also proposes comprehensive strategies for the conservation and management of E. brevicornum, which will further improve the current resource utilization status. The kuenm package was used to optimize the MaxEnt modeling. The temperature seasonality and ground‐frost frequency were regarded as the most important variables. This study determined three significant areas including Qingchuan, Xixiang, and Hanyin, for cultivation such.

ABSTRACT A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-‘microeco’ as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco). An integrated and powerful R package-microeco was developed for researchers to perform data mining of amplicon sequencing in microbial community ecology.

Background Venn diagrams are commonly used to display list comparison. In biology, they are widely used to show the differences between gene lists originating from different differential analyses, for instance. They thus allow the comparison between different experimental conditions or between different methods. However, when the number of input lists exceeds four, the diagram becomes difficult to read. Alternative layouts and dynamic display features can improve its use and its readability. Results jvenn is a new JavaScript library. It processes lists and produces Venn diagrams. It handles up to six input lists and presents results using classical or Edwards-Venn layouts. User interactions can be controlled and customized. Finally, jvenn can easily be embeded in a web page, allowing to have dynamic Venn diagrams. Conclusions jvenn is an open source component for web environments helping scientists to analyze their data. The library package, which comes with full documentation and an example, is freely available at http://bioinfo.genotoul.fr/jvenn .

Demographic rates are shaped by the interaction of past and current environments that individuals in a population experience. Past environments shape individual states via selection and plasticity, and fitness‐related traits (e.g. individual size) are commonly used in demographic analyses to represent the effect of past environments on demographic rates. We quantified how well the size of individuals captures the effects of a population's past and current environments on demographic rates in a well‐studied experimental system of soil mites. We decomposed these interrelated sources of variation with a novel method of multiple regression that is useful for understanding nonlinear relationships between responses and multicollinear explanatory variables. We graphically present the results using area‐proportional Venn diagrams. Our novel method was developed by combining existing methods and expanding upon them. We showed that the strength of size as a proxy for the past environment varied widely among vital rates. For instance, in this organism with an income breeding life history, the environment had more effect on reproduction than individual size, but with substantial overlap indicating that size encompassed some of the effects of the past environment on fecundity. This demonstrates that the strength of size as a proxy for the past environment can vary widely among life‐history processes within a species, and this variation should be taken into consideration in trait‐based demographic or individual‐based approaches that focus on phenotypic traits as state variables. Furthermore, the strength of a proxy will depend on what state variable(s) and what demographic rate is being examined; that is, different measures of body size (e.g. length, volume, mass, fat stores) will be better or worse proxies for various life‐history processes.

Multiplex networks have been widely used to describe the multi-type connections of entities in the real world. However, researches for multiplex networks visualization unilaterally focus on the presentation of topological structure, lacking of specific high-level information presentation for quantitative comparison of interlayer structure. Users cannot participate in the exploration and freely choose the layers (or sub-graphs, regions, etc.) of interest for structural comparison. Contraposing the layer-edge patterns visual analysis tasks of multiplex networks, this paper puts forward a novel solution for exploration and analysis that tightly couples topological structure and high-level patterns. It mainly contains a multi-force directed model to realize the balanced layout of nodes in multi-layer topology, as well as two kinds of high-level patterns of which the visual representations are, respectively, designed by a familiar metaphor—that is, the similar pattern representation based on the area-proportional Venn diagrams and the interaction pattern representation based on the directed arrows. Furthermore, views association is implemented through underlying data sharing and multiple interactions which can be used to gain insights through the creation of selections of interest and produce high-level infographic-style overviews simultaneously. The experiments on real-world data demonstrate the support of the proposed method for layer-edge patterns analysis tasks in multiplex networks and the effectiveness for analyzing the multi-layer structure of multiplex networks.