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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
Big Data-Led Cancer Research, Application, and Insights
James A L Brown1, Triona Ni Chonghaile2, Kyle B Matchett3
1Discipline of Surgery, School of Medicine, The Lambe Institute for Translational Research, National University of Ireland Galway, Galway, Ireland. james.brown@nuigalway.ie.
Abstract:
Insights distilled from integrating multiple big-data or "omic" datasets have revealed functional hierarchies of molecular networks driving tumorigenesis and modifiers of treatment response. Identifying these novel key regulatory and dysregulated elements is now informing personalized medicine. Crucially, although there are many advantages to this approach, there are several key considerations to address. Here, we examine how this big data-led approach is impacting many diverse areas of cancer research, through review of the key presentations given at the Irish Association for Cancer Research Meeting and importantly how the results may be applied to positively affect patient outcomes. Cancer Res; 76(21); 6167-70. ©2016 AACR.
Insights
Integrating big data and omics datasets reveals molecular networks driving cancer and treatment response, informing personalized medicine. This approach impacts diverse cancer research areas and patient outcomes.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Big data and omics integration offers insights into molecular networks.
- Identifying key regulatory elements drives personalized cancer medicine.
- This approach has advantages but requires careful consideration.
Purpose of the Study:
- To examine the impact of big data-driven approaches in cancer research.
- To review key presentations from the Irish Association for Cancer Research Meeting.
- To explore the application of these findings for improved patient outcomes.
Main Methods:
- Review of presentations from the Irish Association for Cancer Research Meeting.
- Analysis of integrated big-data and omics datasets.
- Examination of functional hierarchies in molecular networks.
Main Results:
- Big data integration has revealed molecular networks driving tumorigenesis.
- Key regulatory and dysregulated elements influencing treatment response have been identified.
- The approach is informing personalized medicine strategies.
Conclusions:
- Big data-driven approaches are significantly impacting diverse cancer research fields.
- Understanding molecular networks is crucial for advancing personalized cancer care.
- Application of these insights holds potential for positively affecting patient outcomes.
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