An automated model based approach to test web application using ontology

Motivation: Recent technological developments have facilitated an expansion of microbiome-metabolome studies, in which a set of microbiome samples are assayed using both genomic and metabolomic technologies to characterize the composition of microbial taxa and the concentrations of various metabolites. A common goal of many of these studies is to identify microbial features (species or genes) that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of such microbe-metabolite links. Results: We present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and specific taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and can be customized to incorporate user-defined metabolic pathways. We demonstrate MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, and compare its results with experimentally inferred mechanisms in a dataset describing honeybee gut microbiota. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products. Availability and Implementation: MIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server and R package from www.borensteinlab.com/software_MIMOSA2.html.

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An Approach for Estimation Reliability Model based Web Application Systems

International Journal of Computer Applications ◽

10.5120/2295-2980 ◽

2011 ◽

Vol 18 (7) ◽

pp. 30-33

Author(s):

John Blesswin ◽

Sumy Joseph ◽

Merlin Soosaiya ◽

Priya K V

Keyword(s):

Web Application ◽

Reliability Model ◽

Model Based ◽

Application Systems

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Estimating seroprevalence of SARS-CoV-2 antibodies using three self-reported symptoms: Development of a prediction model based on data from Ischgl, Austria

10.31219/osf.io/jsdx6 ◽

2020 ◽

Author(s):

Jens Lehmann ◽

Johannes M Giesinger ◽

Gerhard Rumpold ◽

Wegene Borena ◽

Ludwig Knabl ◽

...

Keyword(s):

Logistic Regression ◽

Multivariate Analysis ◽

Prediction Model ◽

Regression Model ◽

Web Application ◽

Binary Logistic Regression ◽

Population Level ◽

Limb Pain ◽

The Past ◽

Model Based

We report the development of a regression model to predict prevalence of SARS-CoV-2 antibodies on a population level based on self-reported symptoms.We assessed participant-reported symptoms in the past twelve weeks, as well as presence of SARS-CoV-2 antibodies during a study conducted in April 2020 in Ischgl, Austria. We conducted multivariate binary logistic regression to predict seroprevalence in the sample. Participants (n=451) were on average 47.4 years old (SD 16.8) and 52.5% female. SARS-CoV-2 antibodies were found in n=197 (43.7%) participants. In the multivariate analysis, three significant predictors were included: Odds ratios (OR) for the most predictive categories were: cough (OR 3.34, CI 1.70 - 6.58), gustatory/olfactory alterations (OR 13.78, CI 5.90 - 32.17), and limb pain (OR 2.55, CI 1.20 - 6.50). The AUC was 0.773 (95% CI: 0.727-0.820).Our regression model may be used to estimate seroprevalence on a population-level and a web application is being developed to facilitate use of the model.

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Event-driven web application testing based on model-based mutation testing

Information and Software Technology ◽

10.1016/j.infsof.2015.07.003 ◽

2015 ◽

Vol 67 ◽

pp. 159-179 ◽

Cited By ~ 2

Author(s):

Elahe Habibi ◽

Seyed-Hassan Mirian-Hosseinabadi

Keyword(s):

Web Application ◽

Mutation Testing ◽

Model Based ◽

Web Application Testing ◽

Event Driven

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Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents

Symmetry ◽

10.3390/sym12030354 ◽

2020 ◽

Vol 12 (3) ◽

pp. 354

Author(s):

Tiberiu-Marian Georgescu

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Web Application ◽

Named Entity Recognition ◽

Relation Extraction ◽

Entity Recognition ◽

Model Based ◽

The Way

This paper describes the development and implementation of a natural language processing model based on machine learning which performs cognitive analysis for cybersecurity-related documents. A domain ontology was developed using a two-step approach: (1) the symmetry stage and (2) the machine adjustment. The first stage is based on the symmetry between the way humans represent a domain and the way machine learning solutions do. Therefore, the cybersecurity field was initially modeled based on the expertise of cybersecurity professionals. A dictionary of relevant entities was created; the entities were classified into 29 categories and later implemented as classes in a natural language processing model based on machine learning. After running successive performance tests, the ontology was remodeled from 29 to 18 classes. Using the ontology, a natural language processing model based on a supervised learning model was defined. We trained the model using sets of approximately 300,000 words. Remarkably, our model obtained an F1 score of 0.81 for named entity recognition and 0.58 for relation extraction, showing superior results compared to other similar models identified in the literature. Furthermore, in order to be easily used and tested, a web application that integrates our model as the core component was developed.

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Designing and evaluating dose-escalation studies made easy: The MoDEsT web app

Clinical Trials ◽

10.1177/1740774519890146 ◽

2019 ◽

Vol 17 (2) ◽

pp. 147-156 ◽

Cited By ~ 1

Author(s):

Philip Pallmann ◽

Fang Wan ◽

Adrian P Mander ◽

Graham M Wheeler ◽

Christina Yap ◽

...

Keyword(s):

Dose Escalation ◽

Early Phase ◽

Web Application ◽

Heuristic Algorithms ◽

Single Agent ◽

Decision Procedures ◽

Bayesian Decision ◽

Operating Characteristics ◽

Model Based ◽

Study Designs

Background/aims: Dose-escalation studies are essential in the early stages of developing novel treatments, when the aim is to find a safe dose for administration in humans. Despite their great importance, many dose-escalation studies use study designs based on heuristic algorithms with well-documented drawbacks. Bayesian decision procedures provide a design alternative that is conceptually simple and methodologically sound, but very rarely used in practice, at least in part due to their perceived statistical complexity. There are currently very few easily accessible software implementations that would facilitate their application. Methods: We have created MoDEsT, a free and easy-to-use web application for designing and conducting single-agent dose-escalation studies with a binary toxicity endpoint, where the objective is to estimate the maximum tolerated dose. MoDEsT uses a well-established Bayesian decision procedure based on logistic regression. The software has a user-friendly point-and-click interface, makes changes visible in real time, and automatically generates a range of graphs, tables, and reports. It is aimed at clinicians as well as statisticians with limited expertise in model-based dose-escalation designs, and does not require any statistical programming skills to evaluate the operating characteristics of, or implement, the Bayesian dose-escalation design. Results: MoDEsT comes in two parts: a ‘Design’ module to explore design options and simulate their operating characteristics, and a ‘Conduct’ module to guide the dose-finding process throughout the study. We illustrate the practical use of both modules with data from a real phase I study in terminal cancer. Conclusion: Enabling both methodologists and clinicians to understand and apply model-based study designs with ease is a key factor towards their routine use in early-phase studies. We hope that MoDEsT will enable incorporation of Bayesian decision procedures for dose escalation at the earliest stage of clinical trial design, thus increasing their use in early-phase trials.

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