ShinyCOPASI: a web-based exploratory interface for COPASI models
COPASI is a popular application for simulation and analysis of biochemical networks and their dynamics. While this software is widely used, it works as a standalone application and until now it was not possible for users to interact with its models through the web. We built ShinyCOPASI, a web-based application that allows COPASI models to be explored through a web browser. ShinyCOPASI was written in R with the CoRC package, which provides a high-level R API for COPASI, and the Shiny package to expose it as a web application. The web view provided by ShinyCOPASI follows a similar interface to the standalone COPASI and allows users to explore the details of a model, as well as running a subset of the tasks available in COPASI from within a browser. A generic version allows users to load model files from their computer, while another one pre-loads a specific model from the server and may be useful to provide web access to published models. The application is available at: http://shiny.copasi.org/; and the source code is at: https://github.com/copasi/shinyCOPASI.
Code (1)
Similar Papers 제목 키워드 기반
From Data to Dialogue: Leveraging the Structure of Knowledge Graphs for Conversational Exploratory Search
Exploratory search is an open-ended information retrieval process that aims at discovering knowledge about a topic or domain rather than searching for a specific answer or piece of information. Conversational interfaces …
ArticlesConversational SearchInformation RetrievalKnowledge Graphs+2Conversational Exploratory Search via Interactive Storytelling
Conversational interfaces are likely to become more efficient, intuitive and engaging way for human-computer interaction than today's text or touch-based interfaces. Current research efforts concerning conversational int…
Conversational SearchNavigateQuestion AnsweringSearch Timelines: Visualizing Search History to Enable Cross-Session Exploratory Search
Purpose: The timespan over which exploratory searching can occur, as well as the scope and volume of the search activities undertaken, can make it difficult for searchers to remember key details about their search activi…
pyABC: Efficient and robust easy-to-use approximate Bayesian computation
The Python package pyABC provides a framework for approximate Bayesian computation (ABC), a likelihood-free parameter inference method popular in many research areas. At its core, it implements a sequential Monte-Carlo (…
Intelligent Canvas: Enabling Design-Like Exploratory Visual Data Analysis with Generative AI through Rapid Prototyping, Iteration and Curation
Complex data analysis inherently seeks unexpected insights through exploratory visual analysis methods, transcending logical, step-by-step processing. However, existing interfaces such as notebooks and dashboards have li…
Management