Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genomics

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...

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

You might also read

Related Articles

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

Sort by

Same author

Atlantoaxial subluxation treated with exercise therapy combined with occipito-maxillary band traction: a case report.

Journal of medical case reports·2026

Same author

The Single-Cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors.

Cell genomics·2026

Same author

An AI-Powered Trisomy 21 Research Assistant.

bioRxiv : the preprint server for biology·2026

Same author

Psychological-Symptom Mismatch Phenotypes and Their Clinical Implications in Patients with Chronic Obstructive Pulmonary Disease.

International journal of chronic obstructive pulmonary disease·2026

Same author

Don't stop the heart: a performance analysis of large language models and potassium dosing.

medRxiv : the preprint server for health sciences·2026

Same author

Is Messier Better? Mechanistic Insights toward Ultracryogenic Metal-Ion Batteries.

Journal of the American Chemical Society·2026

Same journal

Predicting Chemotherapy Response from Staging Laparoscopy Images.

medRxiv : the preprint server for health sciences·2026

Same journal

Development and External Validation of a Machine Learning Model for 10-Year Ischemic Stroke Risk Prediction in Diverse Populations.

medRxiv : the preprint server for health sciences·2026

Same journal

MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer's Disease Neuroimaging Initiative.

medRxiv : the preprint server for health sciences·2026

Same journal

Genetic and maternal environmental contributions to estimated fetal weight at 20 weeks gestation compared with birthweight.

medRxiv : the preprint server for health sciences·2026

Same journal

Better immediate declarative memory is associated with forgetting during locomotor adaptation in chronic stroke and in older adults.

medRxiv : the preprint server for health sciences·2026

Same journal

An empirical Bayes framework for burden and dispersion association tests helps prioritize rare variants associated with Alzheimer's disease.

medRxiv : the preprint server for health sciences·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

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

Beyond Identifier Matching: An Empirical Characterization of Failure Modes in Biomedical Knowledge Graph Integration.

Shiyue Hu¹, He Cheng¹, Lucas Gillenwater¹

¹Department of Biomedical Informatics, University of Colorado Anschutz.

Medrxiv : the Preprint Server for Health Sciences

|June 5, 2026

Summary

This summary is machine-generated.

Integrating biomedical knowledge graphs (KGs) using identifier matching is insufficient. Advanced methods expand coverage but reduce clinical accuracy, introducing systematic errors in concept grouping for downstream applications.

Keywords:

ClinicalBERT Hetionet PharmGKB PrimeKG UMLS biomedical informatics clinical embeddings knowledge graphs ontology alignment

More Related Videos

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

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

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Jun 6, 2026

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

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

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

Published on: June 13, 2025

Area of Science:

Biomedical Informatics
Data Integration
Machine Learning

Background:

Biomedical knowledge graphs (KGs) are crucial for machine learning, drug discovery, and EHR augmentation.
Current integration methods often assume identifier (ID) matching is sufficient for combining KGs.
This assumption is challenged by the complexity and heterogeneity of biomedical data.

Purpose of the Study:

To empirically evaluate the effectiveness of integrating multiple biomedical KGs.
To quantify concept overlap and identify failure modes in KG integration pipelines.
To assess the impact of integration methods on the clinical granularity of biomedical data.

Main Methods:

Compared four major biomedical KGs (PrimeKG, Hetionet, UMLS, PharmGKB) using a tiered alignment pipeline.
Employed direct ID matching, cross-ontology bridging, ClinicalBERT grouping, exact name matching, and embedding-based fuzzy matching.
Validated integration results against curated mappings and clinical-genetics case studies.

Main Results:

Pairwise KG coverage is highly asymmetric, with significant variation across node types (e.g., genes vs. diseases).
Advanced integration methods (e.g., ClinicalBERT) increase coverage but introduce systematic errors like over-merging and loss of hierarchical relationships.
Aggregate coverage statistics mask critical losses in clinical resolution, impacting downstream applications.

Conclusions:

Identifier matching alone is an inadequate baseline for biomedical KG integration.
Current integration techniques expand coverage at the expense of clinically meaningful detail.
Future work should report per-type coverage and confidence metrics, not just aggregate rates.