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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,...
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.
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
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Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
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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...

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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From Records to Relationships: An Ontology-Based Knowledge Graph Framework for Cancer Data Interoperability.

Maria Papoutsoglou1, Georgios Meditskos1

  • 1School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.

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

This study introduces an ontology-driven approach to integrate complex healthcare data, including clinical records, text, and imaging, for better cancer analysis. The method enhances data interoperability and explainability, enabling precision oncology insights.

Keywords:
healthcareinteroperabilityknowledge graphsmedical dataontology

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Area of Science:

  • Biomedical Informatics
  • Health Data Science
  • Computational Oncology

Background:

  • Healthcare data is rapidly growing in volume and complexity, posing challenges for integration.
  • Current methods for multimodal healthcare data integration often lack interoperability, reasoning, and explainability.
  • Existing approaches typically focus on either ontology mapping or knowledge graph construction independently.

Purpose of the Study:

  • To develop a foundation for ontology-driven cancer data analysis by integrating multimodal data sources.
  • To address the limitations of current methods in achieving interoperability, reasoning, and explainability for healthcare analytics.
  • To generate interpretable, patient-centered insights for precision oncology.

Main Methods:

  • Utilized knowledge graphs (KGs), SNOMED CT, and DICOM imaging metadata.
  • Developed a methodology to align ontologies and apply reasoning for semantic data integration.
  • Integrated structured clinical data, unstructured text, and imaging data sources.

Main Results:

  • Successfully integrated multimodal healthcare data into a unified, interpretable structure.
  • Demonstrated the generation of explainable, patient-centered insights through a colorectal cancer cohort identification scenario.
  • Achieved enhanced interoperability and reasoning capabilities for healthcare analytics.

Conclusions:

  • The proposed ontology-driven framework provides a robust solution for integrating complex healthcare data.
  • This approach facilitates explainable and interoperable analytics crucial for advancing precision oncology.
  • The methodology offers a foundation for more effective utilization of multimodal health data in cancer research and clinical practice.