Open-source Web-based Tool Research Articles

A higher-order transformation approach to the formalization and analysis of BPMN using graph transformation systems

The Business Process Modeling Notation (BPMN) is a widely used standard notation for defining intra- and inter-organizational workflows. However, the informal description of the BPMN execution semantics leads to different interpretations of BPMN elements and difficulties in checking behavioral properties. In this article, we propose a formalization of the execution semantics of BPMN that, compared to existing approaches, covers more BPMN elements while also facilitating property checking. Our approach is based on a higher-order transformation from BPMN models to graph transformation systems. To show the capabilities of our approach, we implemented it as an open-source web-based tool.

Estimation of time dependent scour depth around circular bridge piers: Application of ensemble machine learning methods

Scour is a major issue which impacts the life of a hydraulic structure. In this work, we have considered a bridge as an example of a hydraulic structure. Scour depth increases or decreases from time to time because of variations in flow scenarios. Scour phenomena have been extensively studied and many empirical as well as data-driven approaches have been proposed to estimate the scour depth. However, there are relatively few studies which can predict the time-dependent scour depth. In this study, we use a laboratory dataset compiled from several sources to predict time-dependent scour depth using ensemble and standalone machine learning methods. For scour depth estimation, various factors such as river bed properties (d50), sigmag, rhos, Ucr), flow properties around a pier (rhof, U, mu, g, y), bridge pier geometry (Al, Sh, Dp), and time (t, tR) are used. In this study, we apply two ensemble methods: Bagging Regressor (BR), AdaBoost Regressor (ABR), and one standalone machine learning method, Support Vector Regression (SVR), for the prediction of time-dependent scour depth. Out of these machine learning methods, both the ensemble methods provide superior predictions in comparison to the standalone variant and empirical equations, viz., Bagging Regressor (r2 = 0.913), AdaBoost Regressor (r2 = 0.887), followed by Support Vector Regressor (r2 = 0.814). We have also developed an open-source web-based tool to predict the time-dependent scour depth with the proposed machine learning methods. The web-based tool is generic enough to work with any dataset and allows the end user to select various input combinations, visualizations, and error metrics.

An open-source web-based tool to perform spatial multicriteria analysis

Spatial multicriteria analysis (SMA) is an analysis technique that uses geographical data and value judgements to provide a systematic analytical approach which can be used for making a decision. Decision making can include identifying risk levels, uncertainty, developing prioritization maps, site selection, habitat suitability models. This model is being widely because it allows the planners to make a balanced decision based on multiple parameters. We have built an open source web-based tool to run SMA which can be accessed at https://www.prioritymaps.com/. This web based tool is user friendly and people from all backgrounds can use it to make decision based models.

On evaluation metrics for medical applications of artificial intelligence

Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model’s performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.

Nirmaan: Dataset generation for multiclass scatterplot studies

This paper presents Nirmaan, an open-source web-based tool for generating synthetic datasets of multiclass blobs for use in research related to scatterplots. We demonstrate how to use Nirmaan to generate datasets in the context of a user study where users must determine the centers of each class, but this tool can be used to generate datasets for other scatterplot tasks as well.

Precision Medicine into Clinical Practice: A Web-Based Tool Enables Real-Time Pharmacogenetic Assessment of Tailored Treatments in Psychiatric Disorders.

The management of neuropsychiatric disorders involves different pharmacological treatments. In order to perform efficacious drug treatments, the metabolism of CYP genes can help to foresee potential drug–drug interactions. The NeuroPGx software is an open-source web-based tool for genotype/diplotype/phenotype interpretation for neuropharmacogenomic purposes. The software provides information about: (i) the genotypes of evaluated SNPs (single nucleotide polymorphisms); (ii) the main diplotypes in CYP genes and corresponding metabolization phenotypes; (iii) the list of neuropsychiatric drugs with recommended dosage adjustment (according to CPIC and DPWG guidelines); (iv) the list of possible (rare) diplotypes and corresponding metabolization phenotypes. The combined application of NeuroPGx software to the OpenArray technology results in an easy, quick, and highly automated device ready to be used in routine clinical practice.

Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.

Mapping stakeholders and policies in response to deliberate biological events

BackgroundRecent infectious disease outbreaks have brought increased attention to the need to strengthen global capacity to prevent, detect, and respond to natural biological threats. However, deliberate biological events also represent a significant global threat, but have received relatively little attention. While the Biological Weapons Convention provides a foundation for the response to deliberate biological events, the political mechanisms to respond to and recover from such an event are poorly defined. MethodsWe performed an analysis of the epidemiological timeline, the international policies triggered as a notional deliberate biological event unfolds, and the corresponding stakeholders and mandates assigned by each policy. FindingsThe results of this analysis identify a significant gap in both policy and stakeholder mandates: there is no single policy nor stakeholder mandate for leading and coordinating response activities associated with a deliberate biological event. These results were visualized using an open source web-based tool published at https://dbe.talusanalytics.com. InterpretationWhile there are organizations and stakeholders responsible for leading security or public health response, these roles are non-overlapping and are led by organizations not with limited interaction outside such events. The lack of mandates highlights a gap in the mechanisms available to coordinate response and a gap in guidance for managing the response. The results of the analysis corroborate anecdotal evidence from stakeholder meetings and highlight a critical need and gap in deliberate biological response policy.

Aequatus: an open-source homology browser.

BackgroundPhylogenetic information inferred from the study of homologous genes helps us to understand the evolution of genes and gene families, including the identification of ancestral gene duplication events as well as regions under positive or purifying selection within lineages. Gene family and orthogroup characterization enables the identification of syntenic blocks, which can then be visualized with various tools. Unfortunately, currently available tools display only an overview of syntenic regions as a whole, limited to the gene level, and none provide further details about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes.FindingsWe present Aequatus, an open-source web-based tool that provides an in-depth view of gene structure across gene families, with various options to render and filter visualizations. It relies on precalculated alignment and gene feature information typically held in, but not limited to, the Ensembl Compara and Core databases. We also offer Aequatus.js, a reusable JavaScript module that fulfills the visualization aspects of Aequatus, available within the Galaxy web platform as a visualization plug-in, which can be used to visualize gene trees generated by the GeneSeqToFamily workflow.

An Open-Source Data Science Platform for the Electrochemical Community

It is no exaggeration to say that the lithium-ion battery has revolutionized the world, and the importance of electrochemistry has been brought to the fore as we look to improve batteries for electric vehicles, biomedical devices, and other critical applications. However, electrochemical studies – particularly on batteries – are conducted with software tools that were not designed for the scale and the importance of the energy storage industry today. The software provided by hardware vendors often does not readily provide the answers we need, or even allow us to ask the right questions. Legacy software tools are often no better and suffer from severe stability issues. In industry, time is critical and most data goes unanalyzed, putting manufacturers at risk. Other firms build in-house tools that constantly reinvent the wheel. We believe the community would be better served by working together with tools commensurate with the tasks we face. In recent years, several open-source software and hardware tools have been developed by the electrochemical community. However, most have only garnered modest traction. We believe this is driven by the high barrier of adoption, since most electrochemists are not also software developers, and because a significant amount of time and resources must be dedicated to supporting such projects. Here we present an open-source web-based tool that lays the foundation for collaboration, and addresses the issues described above. Our platform is designed to accomplish the following: 1) Automate rote data analysis. Hours of analysis can be done in minutes or seconds to generate standard plots and statistics for cell capacity, voltage profiles, and cyclic voltammetry, as well as more involved analyses like incremental capacity or other user-defined functions. This is particularly important for batteries where both the number of electrochemical cells and cycles per cell may be very large and cumbersome to analyze thoroughly. 2) Reduce the barrier to enter and contribute to the platform. We provide APIs for a common framework for others to build upon. This saves time compared to developing a one-off application from scratch, and allows for greater impact when packaged together with a suite of other tools. 3) Catalyze electrochemical data science. The growing field of the data science allows for new tools to be developed to ask new questions and glean previously invisible insights from battery performance data. We believe this software platform provides the missing infrastructure required to meet the demands of the electrochemical community today, and rise to the challenges of tomorrow.

The Ark: a customizable web-based data management tool for health and medical research.

The Ark is an open-source web-based tool that allows researchers to manage health and medical research data for humans and animals without specialized database skills or programming expertise. The system provides data management for core research information including demographic, phenotype, biospecimen and pedigree data, in addition to supporting typical investigator requirements such as tracking participant consent and correspondence, whilst also being able to generate custom data exports and reports. The Ark is 'study generic' by design and highly configurable via its web interface, allowing researchers to tailor the system to the specific data management requirements of their study. Source code for The Ark can be obtained freely from the website https://github.com/The-Ark-Informatics/ark/ . The source code can be modified and redistributed under the terms of the GNU GPL v3 license. Documentation and a pre-configured virtual appliance can be found at the website http://sphinx.org.au/the-ark/ . adrianb@unimelb.edu.au. Supplementary data are available at Bioinformatics online.

Merlin: Computer-Aided Oligonucleotide Design for Large Scale Genome Engineering with MAGE.

Genome engineering technologies now enable precise manipulation of organism genotype, but can be limited in scalability by their design requirements. Here we describe Merlin ( http://merlincad.org ), an open-source web-based tool to assist biologists in designing experiments using multiplex automated genome engineering (MAGE). Merlin provides methods to generate pools of single-stranded DNA oligonucleotides (oligos) for MAGE experiments by performing free energy calculation and BLAST scoring on a sliding window spanning the targeted site. These oligos are designed not only to improve recombination efficiency, but also to minimize off-target interactions. The application further assists experiment planning by reporting predicted allelic replacement rates after multiple MAGE cycles, and enables rapid result validation by generating primer sequences for multiplexed allele-specific colony PCR. Here we describe the Merlin oligo and primer design procedures and validate their functionality compared to OptMAGE by eliminating seven AvrII restriction sites from the Escherichia coli genome.

Abstract B1-35: Enrichr2: Next generation gene set enrichment analysis web-based tool

Abstract Background: System-wide profiling of genes and proteins in mammalian cells produces lists of differentially expressed genes/proteins needing to be further analyzed for their collective functions in order to extract new knowledge. Once lists of genes or proteins are generated from such experiments, they are compared to existing annotated lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set library databases have been developed, there is still room for improvement. Enrichr2 is a major update of the published popular tool Enrichr. Methods: Enrichr2's architecture has been redesigned to be highly scalable. The new database centric design allows Enrichr2 to perform enrichment analysis within seconds while accommodating for the rapid growth of our gene-set library database. Recent additions to our gene-set library database include: disease signatures from GEO, drug perturbations from GEO, kinase perturbations from GEO and L1000, as well as tissue protein expression from two recent publications. Principal Angle Enrichment Analysis has been implemented and is one of the available enrichment analysis scoring methods. Enrichr2 can be restricted to only consider a subspace of the genome, and it accepts input formats that are crisp or fuzzy gene sets, probe IDs from microarrays, BED files and SOFT files. Gene synonyms are also recognized and automatically converted. Users can upload their own gene-set libraries as well as access the legacy versions of our libraries. Results: Enrichr2 is an easy to use intuitive enrichment analysis web-based tool providing various visualization summaries of collective functions of gene lists. Enrichr2 includes many new gene-set libraries, alternative approaches to rank enriched terms, and various new interactive visualization approaches to display enrichment results. We applied Enrichr2 to analyze many of the gene expression datasets available at The Cancer Genome Atlas (TCGA). Global visualization of such enrichment analysis results provides a global view of the biological molecular mechanisms driving tumor behavior across cancers at the individual patient level. Conclusions: Enrichr2 is emerging as a leading search engine for annotated gene lists, taking gene set enrichment analysis to a new level. Enrichr is an open source web-based tool available online at: http://amp.pharm.mssm.edu/Enrichr Note: This abstract was not presented at the conference. Citation Format: Matthew R. Jones. Enrichr2: Next generation gene set enrichment analysis web-based tool. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-35.

Drug/Cell-line Browser: interactive canvas visualization of cancer drug/cell-line viability assay datasets.

Recently, several high profile studies collected cell viability data from panels of cancer cell lines treated with many drugs applied at different concentrations. Such drug sensitivity data for cancer cell lines provide suggestive treatments for different types and subtypes of cancer. Visualization of these datasets can reveal patterns that may not be obvious by examining the data without such efforts. Here we introduce Drug/Cell-line Browser (DCB), an online interactive HTML5 data visualization tool for interacting with three of the recently published datasets of cancer cell lines/drug-viability studies. DCB uses clustering and canvas visualization of the drugs and the cell lines, as well as a bar graph that summarizes drug effectiveness for the tissue of origin or the cancer subtypes for single or multiple drugs. DCB can help in understanding drug response patterns and prioritizing drug/cancer cell line interactions by tissue of origin or cancer subtype. DCB is an open source Web-based tool that is freely available at: http://www.maayanlab.net/LINCS/DCB CONTACT: avi.maayan@mssm.edu Supplementary data are available at Bioinformatics online.