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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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Model Approaches for Pharmacokinetic Data: Physiological Models

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Pharmacokinetic Models: Overview

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Model Approaches for Pharmacokinetic Data: Compartment Models

Model Approaches for Pharmacokinetic Data: Compartment Models

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

An ontology-based and distributed KDD model for biomedical sources.

David Perez-Rey¹, Alberto Anguita, Jose Crespo

¹Biomedical Informatics Group, Artificial Intelligence Lab. Facultad de Informática, Universidad Politécnica de Madrid, Spain.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|August 13, 2008

Summary

This study introduces an ontology-based approach for knowledge discovery in biomedical research. It enhances data preprocessing from diverse, distributed sources, overcoming limitations of traditional methods.

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Area of Science:

Biomedical Informatics
Data Science
Knowledge Discovery

Background:

Modern biomedical research relies on accessing distributed, heterogeneous data sources.
Traditional knowledge discovery models struggle with decentralized databases.
Ontologies offer a solution for managing data across KDD phases.

Purpose of the Study:

To present a novel ontology-based knowledge discovery and data mining (KDD) model.
To improve the data preprocessing stage for heterogeneous data sources.
To address the challenges of distributed data environments in biomedical research.

Main Methods:

Development of a new ontology-based KDD framework.
Application of ontologies across all phases of the KDD process.
Focus on enhancing data preprocessing for improved data quality.

Main Results:

The proposed model facilitates access to heterogeneous and remote data.
Ontologies enable effective data integration and preprocessing in distributed settings.
Improved data quality for subsequent knowledge discovery tasks.

Conclusions:

Ontology-based KDD approaches are effective for distributed biomedical data.
The presented model enhances data preprocessing, a critical KDD phase.
This approach supports more robust knowledge discovery in complex research environments.