Related Experiment Video

Updated: Jul 18, 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

Analysis of the evolution of COVID-19 disease understanding through temporal knowledge graphs

Alessandro Negro1, Fabio Montagna1, Michael N Teng2

  • 1Graph Aware Ltd., London, United Kingdom.

Frontiers in Research Metrics and Analytics
|August 21, 2023
PubMed

Insights

This study introduces a novel knowledge graph to rapidly analyze evolving scientific knowledge during disease outbreaks. It enables faster access to information for developing treatments and vaccines, improving pandemic preparedness.

Area of Science:

  • Life Sciences
  • Bioinformatics
  • Computational Biology

Background:

  • The COVID-19 pandemic exposed critical gaps in utilizing existing biological knowledge and analyzing new disease information for rapid response.
  • Inefficient knowledge management hinders the swift development of treatments, vaccines, and compounds against novel pathogens.
  • Revolutionizing global preparedness requires overcoming these challenges in knowledge assimilation and analysis.

Purpose of the Study:

  • To introduce a novel knowledge graph application serving as a life science knowledge repository and analytics platform.
  • To demonstrate extracting time-bounded key concepts from scholarly articles for a unified temporal source of truth on COVID-19.
  • To uncover evolving disease dynamics and researchers' understanding through time-sensitive insights.

Main Methods:

  • Developed a knowledge graph application integrating life science data.
  • Leveraged existing ontologies to extract time-bounded key concepts from evolving scholarly articles.
  • Applied temporal analysis to a subset of the knowledge graph (temporal keywords knowledge graph).

Main Results:

  • Created a single, temporal, connected source of truth for COVID-19 knowledge.
  • Enabled prompt access to current knowledge for both human and machine analysis.
  • Extracted time-sensitive insights into evolving disease dynamics and research trends.

Conclusions:

  • The novel knowledge graph application significantly enhances the ability to rapidly analyze and utilize scientific knowledge during outbreaks.
  • This approach facilitates quicker development of countermeasures and improves global pandemic preparedness.
  • The temporal analysis of the knowledge graph provides valuable insights into disease evolution and research landscapes.
Keywords:
COVID-19keywordskeywords analysisknowledge graphsknowledge representationtemporal graph analysisunstructured data

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems05:47

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

Published on: June 13, 2025

277
Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 112:00

Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 1

Published on: April 8, 2009

10.4K

Related Concept Videos

Viral Mutations00:36

Viral Mutations

A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
32.4K
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
465
What is Evolutionary History?02:35

What is Evolutionary History?

Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.
36.6K
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.3K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
152