Isis Narvaez-Bandera, PhD

My master's study involved an integrative data-driven approach to identifying molecular patterns in breast cancer patients. This work was motivated because, despite advances in genomic technologies, the molecular understanding of breast cancer is still insufficient. However, acquiring knowledge about many diseases is glimpsed by characterizing interactions and genomic patterns. Therefore, my work presented a multi-stage data mining approach to discriminate breast cancer subtypes by integrating highly dimensional data from different genomic platforms. In addition, I provided a new metric which was named: IBIF-RF (Importance Between Interactive Features using Random Forest).). IBIF identified and classified the interactions between variables using a prevalence criterion and a well-known classifier in the genomics area, such as Random Forest. This metric comprehensively ranked and classified higher-order interactions between variables without prior knowledge of possible combinations, allowing the identification of important interactions gene lists to discriminate breast cancer subtypes. After completing a master’s degree, I was excited to continue contributing to bioinformatics. Thus, I enrolled in the Bioengineering Ph.D. program.