Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Single-cell profiling reveals that dynamic lung immune responses distinguish protection from susceptibility to tuberculosis.

PLoS pathogens·2026
Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·2026
Same author

Variogram Modeling of Spatially Variant Early Response to Therapy in Advanced Non-Small Cell Lung Cancer.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

KGBN: Augmenting and optimizing logical gene regulatory networks using knowledge graphs.

bioRxiv : the preprint server for biology·2026
Same author

Multiparametric MRI Markers Associated with Breast Cancer Risk in Women with Dense Breasts.

Cancers·2025
Same author

Verification and reproducible curation of the BioModels repository.

PLoS computational biology·2025
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026
Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026
See all related articles

Related Experiment Video

Updated: Jun 11, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Multiple ontologies in action: composite annotations for biosimulation models.

John H Gennari1, Maxwell L Neal, Michal Galdzicki

  • 1Department of Biomedical & Health Informatics, University of Washington, USA. gennari@uw.edu

Journal of Biomedical Informatics
|July 6, 2010
PubMed
Summary
This summary is machine-generated.

Composite annotations integrate multiple ontologies for biological models, enhancing clarity and enabling model merging. This approach leverages SemGen software to improve interoperability in physiological biosimulation.

More Related Videos

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Related Experiment Videos

Last Updated: Jun 11, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Existing ontologies offer detailed semantics for biological entities but lack a unified source for all biological phenomena.
  • Physiological biosimulation models often require annotations drawing from multiple reference ontologies to convey comprehensive semantics.
  • A need exists for methods to connect and link orthogonal reference ontologies to maximize their utility.

Purpose of the Study:

  • To introduce and demonstrate the utility of composite annotations for capturing physics-based semantics in biosimulation models.
  • To present SemGen, a software tool that utilizes composite annotations for semantics-based model composition.
  • To facilitate model merging and interoperability by providing semantic expressivity for complex model features.

Main Methods:

  • Development of composite annotations that access multiple ontologies to define model variable semantics.
  • Implementation of composite annotations within SemGen, a semantics-based model composition software.
  • Application of composite annotations to demonstrate utility in model merging and interoperability.

Main Results:

  • Composite annotations provide enhanced semantic expressivity, disambiguating complex features in biosimulation models.
  • The SemGen software effectively utilizes composite annotations for semantics-based model composition.
  • Demonstrated utility of composite annotations in assisting with model merging and improving interoperability.

Conclusions:

  • Composite annotations offer a mechanism for leveraging multiple reference ontologies to capture comprehensive model semantics.
  • SemGen and composite annotations provide a practical solution for connecting and linking orthogonal ontologies.
  • The developed approach enhances the potential of diverse ontologies for biosimulation and biological research.