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

37.0K
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...
37.0K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

19.3K
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.
19.3K
Classification of Illness01:17

Classification of Illness

7.8K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.8K
Ogive Graph01:07

Ogive Graph

5.8K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
5.8K
Gene Families01:57

Gene Families

2.7K
2.7K
Protein Networks02:26

Protein Networks

2.4K
2.4K

You might also read

Related Articles

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

Sort by
Same author

Systematic identification of rare disease patients in electronic health records enables evaluation of clinical outcomes.

Scientific reports·2026
Same author

CURE ID: A Platform to Collect Real-World Treatment Data for Drug Repurposing in Rare Genetic Disorders.

American journal of medical genetics. Part C, Seminars in medical genetics·2026
Same author

Mondo: integrating disease terminology across communities.

Genetics·2025
Same author

Aligning Orphanet Classification to Identify Disease Characteristics among Rare Disease Clusters.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2025
Same author

Scientific Evidence Based Knowledge Graph in Rare Diseases.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine·2025
Same author

Impact of Preexisting Rare Diseases on COVID-19 Severity, Reinfection, and Long COVID, and the Modifying Effects of Vaccination and Antiviral Therapy: A Retrospective Study from the N3C Data Enclave.

medRxiv : the preprint server for health sciences·2025
Same journal

Cross-linguistic patterns of cognitive biases in large language models: a comparative study in English, Hebrew, and Russian.

Frontiers in artificial intelligence·2026
Same journal

From human-like AI to user adoption: the role of trust, attitude, and social influence in shaping behavioral intention.

Frontiers in artificial intelligence·2026
Same journal

Building large-scale English-Romanian literary translation resources with open models.

Frontiers in artificial intelligence·2026
Same journal

Editorial: GenAI in healthcare: technologies, applications and evaluation.

Frontiers in artificial intelligence·2026
Same journal

Logic, inference, understanding: cross-domain generalization for generative language models.

Frontiers in artificial intelligence·2026
Same journal

Label tree semantic losses for rich multi-class medical image segmentation.

Frontiers in artificial intelligence·2026
See all related articles

Related Experiment Video

Updated: Aug 30, 2025

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

1.7K

Rare disease-based scientific annotation knowledge graph.

Qian Zhu1, Chunxu Qu2, Ruizheng Liu2

  • 1Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, United States.

Frontiers in Artificial Intelligence
|August 29, 2022
PubMed
Summary
This summary is machine-generated.

Researchers created a knowledge graph from rare disease (RD) publications to improve understanding and support further research. This structured data aids in accessing the full spectrum of scientific findings for rare diseases.

Keywords:
PubMedknowledge graphnatural language processingrare disease (RD)scientific annotations

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

547
Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

9.8K

Related Experiment Videos

Last Updated: Aug 30, 2025

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

1.7K
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

547
Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

9.8K

Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Medical Informatics

Background:

  • Rare diseases (RDs) present challenges due to low prevalence and limited data for research.
  • Advancements in big data analytics, particularly in genetics and genomics, have significantly improved the understanding of RDs.
  • A growing body of RD-related publications offers potential for scientific insight and further investigation.

Purpose of the Study:

  • To systematically analyze, semantically annotate, and categorize RD-related PubMed articles.
  • To integrate semantic annotations into a knowledge graph (KG) for enhanced data accessibility.
  • To demonstrate the scientific utility of the KG for rare disease research through case studies.

Main Methods:

  • Systematic analysis of RD-related publications from PubMed.
  • Semantic annotation and scientific categorization of analyzed articles.
  • Integration of annotations into a Neo4j-hosted knowledge graph using a predefined data model.

Main Results:

  • A comprehensive knowledge graph was constructed from RD-related literature.
  • The KG successfully integrated semantic annotations, enabling structured data exploration.
  • Case studies demonstrated the KG's capability to reveal scientific contributions in rare disease research.

Conclusions:

  • The developed knowledge graph provides a valuable resource for accessing and understanding the scientific landscape of rare diseases.
  • The study highlights the potential of leveraging big data analytics and knowledge graphs to overcome data limitations in rare disease research.
  • Future work includes expanding the KG with more publications and diverse data resources to further support RD investigation.