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

Genomics02:02

Genomics

41.8K
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...
41.8K
Protein Networks02:26

Protein Networks

4.7K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.7K
Ligand Binding Sites02:40

Ligand Binding Sites

15.8K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
15.8K

You might also read

Related Articles

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

Sort by
Same author

Structured reasoning failures compromise LLM interpretation of clinical oncology notes.

NPJ digital medicine·2026
Same author

Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Large Language Models for Postoperative Decision Support: A Comparative Analysis.

Journal of medical Internet research·2026
Same author

Federated target trial emulation for time-to-event outcomes via POLARIS: Pooled-equivalent One-shot Likelihood Aggregation for Real-world Inference in Survival.

Research square·2026
Same author

Optimizing Retrieval-Augmented Generation (RAG) in clinical medicine: methods and performance evaluation.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

From "negative" trial to positive clinical impact: mitigating eligibility criteria-induced temporal selection bias in emulated clinical trials.

npj health systems·2026
Same author

AI-Generated Avatar Videos for Postoperative Patient Education Among Health Care Workers: Pilot Randomized Controlled Trial.

JMIR perioperative medicine·2026
Same journal

LabSage: Structural-Semantic Decoupling for Enhanced Retrieval-Augmented Generation in Clinical Laboratories.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

Evaluating Representation Embeddings from LLMs and Time-Series Foundation Models for Wearable Accelerometer-Based Health Prediction.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

Mapping the Storm: Linking Tornado Paths to Emergency Room Surges Through Geocoded Patient Data.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
See all related articles

Related Experiment Video

Updated: Apr 5, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

2.3K

Integrating Multiple On-line Knowledge Bases for Disease-Lab Test Relation Extraction.

Yaoyun Zhang1, Ergin Soysal1, Sungrim Moon1

  • 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|August 26, 2015
PubMed
Summary
This summary is machine-generated.

This study created a computable knowledge base linking diseases and lab tests using online resources. This resource enhances biomedical informatics applications by providing valuable disease-lab test relationship data.

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.9K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.7K

Related Experiment Videos

Last Updated: Apr 5, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

2.3K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.9K
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.7K

Area of Science:

  • Biomedical Informatics
  • Knowledge Representation
  • Natural Language Processing

Background:

  • Developing a comprehensive knowledge base of disease-lab test relations is crucial for biomedical informatics.
  • Existing resources may be fragmented or not readily computable.
  • There is a need for integrated, accessible disease-laboratory test relationship data.

Purpose of the Study:

  • To establish an initial computable knowledge base of disease and lab test relations.
  • To integrate data from three public online resources: LabTestsOnline, MedlinePlus, and Wikipedia.
  • To demonstrate a method for extracting and organizing disease-lab test relationships.

Main Methods:

  • Utilized MetaMap for identifying disease and lab test concepts.
  • Developed source-specific rules to determine relations between identified concepts.
  • Integrated data from LabTestsOnline, MedlinePlus, and Wikipedia.
  • Evaluated relation extraction precision and recall against a reference dataset.

Main Results:

  • Achieved high precision in relation extraction, with Wikipedia showing 87% precision.
  • Combined sources yielded a recall of 51.40% compared to a reference subset.
  • Identified additional disease-lab test relations not present in traditional reference books.

Conclusions:

  • Public online resources can be effectively integrated to build a computable disease-lab test knowledge base.
  • The developed knowledge base is a valuable resource for biomedical informatics.
  • Online resources complement traditional reference books for comprehensive knowledge base construction.