Luis Rueda, Ph.D.

Position: Professor, School of Computer Science
Affiliation: University of Windsor


Areas of Expertise: Machine learning methods in discovery of cancer biomarkers; prostate cancer progression and location; breast cancer subtypes, progression, survivability and drug repurposing

Brief Description of Research:

Dr. Luis Rueda’s main research is focused on devising shallow and deep machine learning approaches for identifying and studying cancer biomarkers. His team design and use sophisticated artificial intelligence algorithms to analyze massive amounts of genomics, transcriptomics and interactomics data in different types of cancer, studying the relevance of the findings in existing literature via natural language processing schemes. His main works are on progression of prostate cancer, assessment of aggressiveness and location of prostate tumours, progression of bladder cancer, as well as clinical stages of breast cancer such as subtypes, progression, survivability and drug repurposing.

5 Select Publications:

Hamzeh, O., Alkhateeb, A., Zheng, J., Kandalam, S., and Rueda, L. (2019). Prediction of Tumor Location in Prostate Cancer Tissue using a Machine Learning System on Gene Expression Data. BMC Bioinformatics. In Press.

Tabl, A.A., Alkhateeb, A., El-Maraghy, W., Rueda, L., and Ngom, A. (2019). A MachineLearning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer.Frontiers in Genetics, 10, 256.

Tabl, A.A., Alkhateeb, A., Rueda, L., El-Maraghy, W., and Ngom, A. (2018). A NovelApproach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies.Evolutionary Bioinformatics, 14, 117.

Firoozbakht, F., Rezaeian, I., D’Agnillo, M., Porter, L., Rueda, L., and Ngom, A. (2017). An Integrative Approach for Identifying Network Biomarkers of Breast Cancer Subtypes Using Genomics, Interactomics and Transcriptomics Data. Journal of Computational Biology,4(8),756-766.

Rezaeian, I., Tavakoli, A., Cavallo-Medved, D., Porter, L., and Rueda, L. (2016). A Model Used to Detect Differential Splice Junctions as Biomarkers in Prostate Cancer from RNA-seq Data.Journal of Biomedical Informatics, 60,422-430.