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Related Concept Videos

Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Cancer02:18

Cancer

Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.

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Related Experiment Video

Updated: May 24, 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

A Knowledge Graph to Represent and Predict Cancer Mechanistic Associations.

Mehrana Calagari1, Samina Abidi2, Syed Sibte Raza Abidi1

  • 1NICHE Research Group, Faculty of Computer Science, Dalhousie University, Canada.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-driven cancer knowledge graph (KG) built from millions of research articles. The KG reveals new mechanistic relationships and cancer pathways, aiding personalized cancer treatment discovery.

Keywords:
CancersKnowledge GraphMechanistic RelationshipsPositive-Unknown Learning

Related Experiment Videos

Last Updated: May 24, 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

Area of Science:

  • Computational biology
  • Bioinformatics
  • Cancer research

Background:

  • Cancer incidence is influenced by complex mechanistic relationships.
  • Understanding these mechanisms is crucial for developing personalized cancer interventions.
  • Artificial intelligence (AI)-driven biomedical knowledge graphs (KGs) can integrate multifaceted medical knowledge.

Purpose of the Study:

  • To develop and present a cancer-specific knowledge graph (KG).
  • To discover, represent, and visualize mechanistic relationships between cancer, therapeutic agents, and biomedical concepts.
  • To identify novel cancer-related interactions and pathways using AI.

Main Methods:

  • Constructed a cancer KG by analyzing over 2.5 million PubMed articles.
  • Abstracted 7 distinct cancer-related entities and 30 relationship types.
  • Employed link prediction techniques to identify missing interactions within the KG.

Main Results:

  • Successfully developed a comprehensive cancer knowledge graph.
  • Identified novel mechanistic relationships between cancer entities.
  • Discovered extended cancer pathways by predicting missing interactions.

Conclusions:

  • AI-driven KGs are effective tools for uncovering complex biological mechanisms in cancer.
  • The developed cancer KG facilitates the discovery of new therapeutic targets and pathways.
  • This approach supports advancements in personalized cancer medicine.