Integrating Text Mining, Data Mining, and Network Analysis to Analyze Biomarker Trends in Prostate Cancer and Breast Cancer
Abstract
Cancer is a serious disease which has many types and affects many people. One goal of biomedical researchers is to find genetic biomarkers for diagnosis and prognosis of cancer. Since there is already a vast amount of scientific publications on cancer, computational methods can be used to find hidden patterns from literature. This thesis presents a framework which investigates existing literature data by integrating text mining, data mining, and network analysis. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and the interest of various research groups in different countries. Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some of our results have been validated against results from other tools that predict gene relations and functions.
Description
Keywords
Bioinformatics, Computer Science
Citation
Jurca, G. (2016). Integrating Text Mining, Data Mining, and Network Analysis to Analyze Biomarker Trends in Prostate Cancer and Breast Cancer (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26581