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Creation of Reversible Cholestatic Rat Model

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Published on: May 21, 2011

StreamChol: a web-based application for predicting cholestasis.

Pablo Rodríguez-Belenguer¹, Emilio Soria-Olivas², Manuel Pastor³

¹Research Programme On Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Medical Research Institute, Barcelona, Spain.

Journal of Cheminformatics

|January 22, 2025

Summary

This summary is machine-generated.

StreamChol is a new open-source software enabling mechanistic model development for predicting cholestasis. This user-friendly tool integrates pharmacokinetic and machine learning analyses without requiring programming knowledge.

Keywords:

Framework In-silico toxicology QSAR Web interfaces

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A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

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Published on: March 14, 2019

Area of Science:

Toxicology
Computational Biology
Software Development

Background:

Predictive modeling for toxicological endpoints like cholestasis is crucial.
Integrating pharmacokinetic (PK) analyses with machine learning (ML) models offers a powerful approach.
Existing tools may lack user-friendliness or seamless integration capabilities.

Purpose of the Study:

To introduce StreamChol, a novel software for developing and applying mechanistic models to predict cholestasis.
To provide a user-friendly interface for integrating PK and ML models.
To offer a streamlined workflow for deploying predictive models via Docker.

Main Methods:

StreamChol is developed as a Streamlit application.
It facilitates the integration of pharmacokinetic analyses with Machine Learning models.
Docker containerization is used for simplified deployment across different environments.

Main Results:

StreamChol enables the prediction of cholestasis using mechanistic models.
The software allows for the integration of PK and ML models for toxicological predictions.
It provides a user-friendly interface, accessible without programming knowledge.

Conclusions:

StreamChol offers a potent, open-source tool for predictive toxicology.
The software simplifies the development and deployment of mechanistic models for cholestasis.
It provides a complete workflow for creating web platforms combining R and Python for scientific applications.