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Updated: May 9, 2026
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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Using large-scale molecular data sets to improve breast cancer treatment
1Department of Medicine & Dan L Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, 1 Baylor Plaza, BCM 600, Houston, TX 77030, USA.
Breast Cancer Management
|July 23, 2013
Summary
Understanding breast cancer biology through gene alterations can advance patient treatment. This review explores mining public molecular data for biomarkers and therapeutic targets in breast cancer research.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Breast cancer treatment can be improved with a deeper understanding of its molecular biology.
- Extensive genomic data (gene expression, copy number, mutations) from breast tumors are publicly available.
- Integrating diverse molecular datasets is crucial for advancing breast cancer research.
Purpose of the Study:
- To review methods for utilizing public molecular data to identify breast cancer biomarkers and therapeutic targets.
- To present examples of integrative analysis combining diverse datasets for breast cancer research.
Main Methods:
- Review of existing literature on breast cancer molecular profiling.
- Discussion of data mining strategies for genomic datasets.
- Presentation of integrative analysis approaches.
Main Results:
- Publicly available molecular data offers a valuable resource for breast cancer research.
- Integrative analysis can uncover candidate biomarkers and therapeutic targets.
- Specific examples illustrate the potential of combining diverse data types.
Conclusions:
- Leveraging public genomic data through integrative analysis is key to advancing breast cancer understanding and treatment.
- Identifying novel biomarkers and therapeutic targets is achievable by mining comprehensive molecular datasets.
