Network-based Visualization Research Articles

WeSA: a web server for improving analysis of affinity proteomics data.

Protein-protein interaction experiments still yield many false positive interactions. The socioaffinity metric can distinguish true protein-protein interactions from noise based on available data. Here, we present WeSA (Weighted SocioAffinity), which considers large datasets of interaction proteomics data (IntAct, BioGRID, the BioPlex) to score human protein interactions and, in a statistically robust way, flag those (even from a single experiment) that are likely to be false positives. ROC analysis (using CORUM-PDB positives and Negatome negatives) shows that WeSA improves over other measures of interaction confidence. WeSA shows consistently good results over all datasets (up to: AUC=0.93 and at best threshold: TPR=0.84, FPR=0.11, Precision=0.98). WeSA is freely available without login (wesa.russelllab.org). Users can submit their own data or look for organized information on human protein interactions using the web server. Users can either retrieve available information for a list of proteins of interest or calculate scores for new experiments. The server outputs either pre-computed or updated WeSA scores for the input enriched with information from databases. The summary is presented as a table and a network-based visualization allowing the user to remove those nodes/edges that the method considers spurious.

Network-based exploratory data analysis and explainable three-stage deep clustering for financial customer profiling

Effective customer segmentation and communication of these findings to non-experts is a pressing task in the financial services sector, with the potential for widespread applications. This study employs a three-stage dimension reduction and clustering technique to segment a large, high-dimensional dataset, emphasizing explainability and intuitive visualization. We present the high-dimensional data and feature set using novel network-based visualization methods and identify the multi-stage process's optimal configuration. The approach segments 14,837 potential customers, each with 163 categorical and 143 numerical features. The first stage of the dimension reduction process employs deep neural network-based autoencoders. The second and third stage uses a non-neural network-based dimension reduction algorithm and clustering algorithm contingent on clustering performance. Subsequently, game theory-inspired Shapley values are computed for each feature to enhance explainability. The optimal approach involves an autoencoder, isometric mapping to three dimensions, and K-means clustering. Lastly, we derive investment portfolios for each segment to demonstrate an expert system application in financial investment advisory to underscore the importance of explainable segmentations.

Across the Color Line: Using Text Networks to Examine Black and White US Soldiers’ Views on Race and Segregation during World War II

"Across the Color Line" is part of a larger digital project, The American Soldier in World War II. The project has made accessible sixty-five thousand pages of uncensored handwritten commentaries composed by US service members, along with quantitative datasets produced by a novel interdisciplinary US Army research branch. This small organization of leading psychologists and social scientists was tasked with analyzing and reporting on soldier morale, including the rank-and-file effects of segregation and relations between the races. This article showcases initial findings derived from a cross-sectional survey of Black and White soldiers administered in February and March of 1943, only months before race rebellions erupted in Detroit, Los Angeles, Harlem, and elsewhere over the country's maintenance of Jim Crow while the US and its allies were at war to save democracy. Text correlation networks reveal in striking visual detail the effects of segregation on the experiences, habits, and opinions of both White and Black US citizen-soldiers, individually and collectively. Network-based visualizations are particularly effective at revealing the inverted epistemology of American racial thought and of W.E.B. Du Bois's popular characterization of Black America's "double consciousness," or "twoness."

MoSBi: Automated signature mining for molecular stratification and subtyping

The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (molecular signature identification using biclustering), an automated multialgorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We systematically evaluated the performance of 11 available and established biclustering algorithms together with MoSBi. For this, we used transcriptomics, proteomics, and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multialgorithm integration, MoSBi identified robust group and disease-specific signatures across all scenarios, overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that supports biological hypothesis generation. MoSBi is available as an R package and web service to make automated biclustering analysis accessible for application in molecular sample stratification.

NETMAGE: A human disease phenotype map generator for the network-based visualization of phenome-wide association study results.

BackgroundDisease complications, the onset of secondary phenotypes given a primary condition, can exacerbate the long-term severity of outcomes. However, the exact cause of many of these cross-phenotype associations is still unknown. One potential reason is shared genetic etiology—common genetic drivers may lead to the onset of multiple phenotypes. Disease-disease networks (DDNs), where nodes represent diseases and edges represent associations between diseases, can provide an intuitive way of understanding the relationships between phenotypes. Using summary statistics from a phenome-wide association study (PheWAS), we can generate a corresponding DDN where edges represent shared genetic variants between diseases. Such a network can help us analyze genetic associations across the diseasome, the landscape of all human diseases, and identify potential genetic influences for disease complications.ResultsTo improve the ease of network-based analysis of shared genetic components across phenotypes, we developed the humaN disEase phenoType MAp GEnerator (NETMAGE), a web-based tool that produces interactive DDN visualizations from PheWAS summary statistics. Users can search the map by various attributes and select nodes to view related phenotypes, associated variants, and various network statistics. As a test case, we used NETMAGE to construct a network from UK BioBank (UKBB) PheWAS summary statistic data. Our map correctly displayed previously identified disease comorbidities from the UKBB and identified concentrations of hub diseases in the endocrine/metabolic and circulatory disease categories. By examining the associations between phenotypes in our map, we can identify potential genetic explanations for the relationships between diseases and better understand the underlying architecture of the human diseasome. Our tool thus provides researchers with a means to identify prospective genetic targets for drug design, using network medicine to contribute to the exploration of personalized medicine.

PROMISCUOUS 2.0: a resource for drug-repositioning.

The development of new drugs for diseases is a time-consuming, costly and risky process. In recent years, many drugs could be approved for other indications. This repurposing process allows to effectively reduce development costs, time and, ultimately, save patients’ lives. During the ongoing COVID-19 pandemic, drug repositioning has gained widespread attention as a fast opportunity to find potential treatments against the newly emerging disease. In order to expand this field to researchers with varying levels of experience, we made an effort to open it to all users (meaning novices as well as experts in cheminformatics) by significantly improving the entry-level user experience. The browsing functionality can be used as a global entry point to collect further information with regards to small molecules (∼1 million), side-effects (∼110 000) or drug-target interactions (∼3 million). The drug-repositioning tab for small molecules will also suggest possible drug-repositioning opportunities to the user by using structural similarity measurements for small molecules using two different approaches. Additionally, using information from the Promiscuous 2.0 Database, lists of candidate drugs for given indications were precomputed, including a section dedicated to potential treatments for COVID-19. All the information is interconnected by a dynamic network-based visualization to identify new indications for available compounds. Promiscuous 2.0 is unique in its functionality and is publicly available at http://bioinformatics.charite.de/promiscuous2.

Investigations on spreading of PRRSV among swine herds by improved minimum spanning network analysis

In Hungary, the economic losses caused by porcine reproductive and respiratory syndrome virus (PRRSV) led to the launching of a national PRRSV Eradication Program. An important element of the program was investigating the spread of PRRSV among swine herds and the possible ways of introduction by sequencing of the open reading frame 5 (ORF5) gene. However, the classical phylogenetic tree presentation cannot explain several genetic relationships clearly, while more precise visualization can be represented by network tree diagram. In this paper, we describe a practical and easy-to-follow enriched minimum spanning similarity network application for improved representation of phylogenetic relations among viral strains. This method eliminated the necessity of applying a predefined, arbitrary cut-off or computationally extensive algorithms. The network-based visualization allowed processing and visualizing large amount of data equally for the laboratory, private and official veterinarians, and helped identify the potential connections between different viral sequences that support data-driven decisions in the eradication program. By applying network analysis, previously unknown epidemiological connections between infected herds were identified, and virus spreading was analyzed within short period of time. In our study, we successfully built and applied network analysis tools in the course of the Hungarian PRRSV Eradication Program.

Data-driven visualization schema of a materials informatics curriculum: Convergence of materials science and information science

AbstractIn addition to student assessment, curriculum assessment is a critical element to any pedagogy. It helps the educator assess the teaching of concepts, determine what may be lacking, and make changes for continual improvement. Meaningful assessment can be complicated when disciplines converge or when new approaches are implemented. To facilitate this, we present a network-based visualization schema to represent a materials informatics curriculum that combines materials science and data science concepts. We analyze the curriculum using network representations and relevant concepts from graph theory. This reveals established connections, linkages between materials science and data science, and the extent to which different concepts are connected. We also describe how some materials science topics are introduced from a data perspective, and present an illustrative case study from the curriculum.

MIENTURNET: an interactive web tool for microRNA-target enrichment and network-based analysis

BackgroundmiRNAs regulate the expression of several genes with one miRNA able to target multiple genes and with one gene able to be simultaneously targeted by more than one miRNA. Therefore, it has become indispensable to shorten the long list of miRNA-target interactions to put in the spotlight in order to gain insight into understanding the regulatory mechanism orchestrated by miRNAs in various cellular processes. A reasonable solution is certainly to prioritize miRNA-target interactions to maximize the effectiveness of the downstream analysis.ResultsWe propose a new and easy-to-use web tool MIENTURNET (MicroRNA ENrichment TURned NETwork) that receives in input a list of miRNAs or mRNAs and tackles the problem of prioritizing miRNA-target interactions by performing a statistical analysis followed by a fully featured network-based visualization and analysis. The statistics is used to assess the significance of an over-representation of miRNA-target interactions and then MIENTURNET filters based on the statistical significance associated with each miRNA-target interaction. In addition, the holistic approach of the network theory is used to infer possible evidences of miRNA regulation by capturing emergent properties of the miRNA-target regulatory network that would be not evident through a pairwise analysis of the individual components.ConclusionMIENTURNET offers the possibility to consistently perform both statistical and network-based analyses by using only a single tool leading to a more effective prioritization of the miRNA-target interactions. This has the potential to avoid researchers without computational and informatics skills to navigate multiple websites and thus to independently investigate miRNA activity in every cellular process of interest in an easy and at the same time exhaustive way thanks to the intuitive web interface. The web application along with a well-documented and comprehensive user guide are freely available at http://userver.bio.uniroma1.it/apps/mienturnet/without any login requirement.

A multicriteria integral framework for agent-based model calibration using evolutionary multiobjective optimization and network-based visualization

Automated calibration methods are a common approach to agent-based model calibration as they can estimate those parameters which cannot be set because of the lack of information. The modeler requires to validate the model by checking the parameter values before the model can be used and this task is very challenging when the model considers two or more conflicting outputs. We propose a multicriteria integral framework to assist the modeler in the calibration and validation of agent-based models that combines evolutionary multiobjective optimization with network-based visualization, which we believe is the first integral approach to model calibration. On the one hand, evolutionary multiobjective optimization provides several sets of calibration solutions (i.e., parameter values) with different trade-offs for the considered objectives in a single run. On the other hand, network-based visualization is used to better understand the decision space and the set of solutions from the obtained Pareto set approximation. To illustrate our proposal, we face the calibration of three agent-based model examples for marketing which consider two conflicting criteria: the awareness of the brand and its word-of-mouth volume. The final analysis of the calibrated solutions shows how our proposed framework eases the analysis of Pareto sets with high cardinality and helps with the identification of flexible solutions (i.e., those having close values in the design space).

Hyperspectral imaging of human skin aided by artificial neural networks.

We developed a compact, hand-held hyperspectral imaging system for 2D neural network-based visualization of skin chromophores and blood oxygenation. State-of-the-art micro-optic multichannel matrix sensor combined with the tunable Fabry-Perot micro interferometer enables a portable diagnostic device sensitive to the changes of the oxygen saturation as well as the variations of blood volume fraction of human skin. Generalized object-oriented Monte Carlo model is used extensively for the training of an artificial neural network utilized for the hyperspectral image processing. In addition, the results are verified and validated via actual experiments with tissue phantoms and human skin in vivo. The proposed approach enables a tool combining both the speed of an artificial neural network processing and the accuracy and flexibility of advanced Monte Carlo modeling. Finally, the results of the feasibility studies and the experimental tests on biotissue phantoms and healthy volunteers are presented.

NeVOmics: An Enrichment Tool for Gene Ontology and Functional Network Analysis and Visualization of Data from OMICs Technologies.

The increasing number of OMICs studies demands bioinformatic tools that aid in the analysis of large sets of genes or proteins to understand their roles in the cell and establish functional networks and pathways. In the last decade, over-representation or enrichment tools have played a successful role in the functional analysis of large gene/protein lists, which is evidenced by thousands of publications citing these tools. However, in most cases the results of these analyses are long lists of biological terms associated to proteins that are difficult to digest and interpret. Here we present NeVOmics, Network-based Visualization for Omics, a functional enrichment analysis tool that identifies statistically over-represented biological terms within a given gene/protein set. This tool provides a hypergeometric distribution test to calculate significantly enriched biological terms, and facilitates analysis on cluster distribution and relationship of proteins to processes and pathways. NeVOmics is adapted to use updated information from the two main annotation databases: Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). NeVOmics compares favorably to other Gene Ontology and enrichment tools regarding coverage in the identification of biological terms. NeVOmics can also build different network-based graphical representations from the enrichment results, which makes it an integrative tool that greatly facilitates interpretation of results obtained by OMICs approaches. NeVOmics is freely accessible at https://github.com/bioinfproject/bioinfo/.

Probing the chemical\u2013biological relationship space with the Drug Target Explorer

Modern phenotypic high-throughput screens (HTS) present several challenges including identifying the target(s) that mediate the effect seen in the screen, characterizing ‘hits’ with a polypharmacologic target profile, and contextualizing screen data within the large space of drugs and screening models. To address these challenges, we developed the Drug–Target Explorer. This tool allows users to query molecules within a database of experimentally-derived and curated compound-target interactions to identify structurally similar molecules and their targets. It enables network-based visualizations of the compound-target interaction space, and incorporates comparisons to publicly-available in vitro HTS datasets. Furthermore, users can identify molecules using a query target or set of targets. The Drug Target Explorer is a multifunctional platform for exploring chemical space as it relates to biological targets, and may be useful at several steps along the drug development pipeline including target discovery, structure–activity relationship, and lead compound identification studies.

Automation of ReactomeFIViz via CyREST API

Pathway- and network-based approaches project seemingly unrelated genes onto the context of pathways and networks, enhancing the analysis power that cannot be achieved via gene-based approaches. Pathway and network approaches are routinely applied in large-scale data analysis for cancer and other complicated diseases. ReactomeFIViz is a Cytoscape app, providing features for researchers to perform pathway- and network-based data analysis and visualization by leveraging manually curated Reactome pathways and highly reliable Reactome functional interaction network. To facilitate adoption of this app in bioinformatics software pipeline and workflow development, we develop a CyREST API for ReactomeFIViz by exposing some major features in the app. We describe a use case to demonstrate the use of this API in a Python-based notebook, and believe the new API will provide the community a convenient and powerful tool to perform pathway- and network-based data analysis and visualization using our app in an automatic way.

Network Visualization and Analysis of Spatially Aware Gene Expression Data with InsituNet.

In situ sequencing methods generate spatially resolved RNA localization and expression data at an almost single-cell resolution. Few methods, however, currently exist to analyze and visualize the complex data that is produced, which can encode the localization and expression of a million or more individual transcripts in a tissue section. Here, we present InsituNet, an application that converts in situ sequencing data into interactive network-based visualizations, where each unique transcript is a node in the network and edges represent the spatial co-expression relationships between transcripts. InsituNet is available as an app for the Cytoscape platform at http://apps.cytoscape.org/apps/insitunet. InsituNet enables the analysis of the relationships that existbetween these transcripts and can uncover how spatial co-expression profiles change in different regions of the tissue or across different tissue sections.

PhyloPro2.0: a database for the dynamic exploration of phylogenetically conserved proteins and their domain architectures across the Eukarya.

PhyloPro is a database and accompanying web-based application for the construction and exploration of phylogenetic profiles across the Eukarya. In this update article, we present six major new developments in PhyloPro: (i) integration of Pfam-A domain predictions for all proteins; (ii) new summary heatmaps and detailed level views of domain conservation; (iii) an interactive, network-based visualization tool for exploration of domain architectures and their conservation; (iv) ability to browse based on protein functional categories (GOSlim); (v) improvements to the web interface to enhance drill down capability from the heatmap view; and (vi) improved coverage including 164 eukaryotes and 12 reference species. In addition, we provide improved support for downloading data and images in a variety of formats. Among the existing tools available for phylogenetic profiles, PhyloPro provides several innovative domain-based features including a novel domain adjacency visualization tool. These are designed to allow the user to identify and compare proteins with similar domain architectures across species and thus develop hypotheses about the evolution of lineage-specific trajectories.Database URL: http://www.compsysbio.org/phylopro/

Synergy Maps: exploring compound combinations using network-based visualization.

BackgroundThe phenomenon of super-additivity of biological response to compounds applied jointly, termed synergy, has the potential to provide many therapeutic benefits. Therefore, high throughput screening of compound combinations has recently received a great deal of attention. Large compound libraries and the feasibility of all-pairs screening can easily generate large, information-rich datasets. Previously, these datasets have been visualized using either a heat-map or a network approach—however these visualizations only partially represent the information encoded in the dataset.ResultsA new visualization technique for pairwise combination screening data, termed “Synergy Maps”, is presented. In a Synergy Map, information about the synergistic interactions of compounds is integrated with information about their properties (chemical structure, physicochemical properties, bioactivity profiles) to produce a single visualization. As a result the relationships between compound and combination properties may be investigated simultaneously, and thus may afford insight into the synergy observed in the screen. An interactive web app implementation, available at http://richlewis42.github.io/synergy-maps, has been developed for public use, which may find use in navigating and filtering larger scale combination datasets. This tool is applied to a recent all-pairs dataset of anti-malarials, tested against Plasmodium falciparum, and a preliminary analysis is given as an example, illustrating the disproportionate synergism of histone deacetylase inhibitors previously described in literature, as well as suggesting new hypotheses for future investigation.ConclusionsSynergy Maps improve the state of the art in compound combination visualization, by simultaneously representing individual compound properties and their interactions. The web-based tool allows straightforward exploration of combination data, and easier identification of correlations between compound properties and interactions.

NaviCell Web Service for network-based data visualization.

Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of ‘omics’ data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases.

High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their “thematic” features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be “abnormally large,” can be partially explained by the flow of news. In this sense, our results prove that there is no “excess trading,” when restricting to times when news is genuinely novel and provides relevant financial information.

FluxMap: A VANTED add-on for the visual exploration of flux distributions in biological networks

BackgroundThe quantification of metabolic fluxes is gaining increasing importance in the analysis of the metabolic behavior of biological systems such as organisms, tissues or cells. Various methodologies (wetlab or drylab) result in sets of fluxes which require an appropriate visualization for interpretation by scientists. The visualization of flux distributions is a necessary prerequisite for intuitive flux data exploration in the context of metabolic networks.ResultsWe present FluxMap, a tool for the advanced visualization and exploration of flux data in the context of metabolic networks. The template-based flux data import assigns flux values and optional quality parameters (e. g. the confidence interval) to biochemical reactions. It supports the discrimination between mass and substance fluxes, such as C- or N-fluxes. After import, flux data mapping and network-based visualization allow the interactive exploration of the dataset. Various visualization options enable the user to adapt layout and network representation according to individual purposes.ConclusionsThe Vanted add-on FluxMap comprises a comprehensive set of functionalities for visualization and advanced visual exploration of flux distributions in biological networks. It is available as a Java open source tool from http://www.vanted.org/fluxmap.