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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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A Multiomics Graph Database System for Biological Data Integration and Cancer Informatics.

Ishwor Thapa¹, Hesham Ali¹

¹College of Information Science and Technology, University of Nebraska at Omaha, Omaha, Nebraska, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|August 14, 2020

Summary

This study develops an integrated graph database model to combine diverse multiomics data, including genomics and transcriptomics, for enhanced biological insights. The model facilitates information retrieval for complex biological processes and cancer research.

Keywords:

cancer informatics data integration graph database multiomics

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Multiomics data from epigenetics, genomics, transcriptomics, and proteomics are heterogeneous.
High-throughput technologies enable integrated analysis of DNA methylation, copy number variation (CNV), mutation, gene expression, and miRNA expression.
Radiomics is emerging, with potential integration of tissue image data into multiomics studies.

Purpose of the Study:

To develop a model for integrating diverse multiomics data.
To enable easy retrieval of biologically relevant information.
To address the challenge of multiomics data integration.

Main Methods:

Enrichment of a previous graph database model.
Inclusion of gene expression, miRNA expression, DNA methylation, mutation, CNV, and clinical data.
Integration of tissue slide image information.

Main Results:

The enriched graph database model successfully stores and integrates multiomics data types.
Demonstrated the model's functionality using data from The Cancer Genome Atlas (TCGA).
Successfully integrated data from three cancer types.

Conclusions:

The developed graph database model provides a robust framework for multiomics data integration.
Facilitates the extraction of meaningful biological information from complex datasets.
Supports future research in cancer biology and precision medicine.