Ph.D., Professor, School of Computer Science, University of Windsor
Luis Rueda is a Full Professor in the School of Computer Science. University of Windsor, Canada. Being a PhD from Carleton University, his research interests are focused on theoretical and applied machine learning and pattern recognition, with applications in transcriptomics, interactomics, genomics, and cancer biomarkers. He holds three patents on data encryption and has published more than 120 publications in prestigious journals and conferences in machine learning and bioinformatics. He is a Senior Member of the IEEE, and a Member of the Association for Computing Machinery, the International Society for Computational Biology, and the Windsor Cancer Research Group.
AI and Data Sciences Showcase: Machine Learning in Finding Biomarkers from Multiple Datasets and Clinical Variables
Biomarker discovery from multiple datasets containing multi-omics data on varied clinical variables becomes a challenge. An example is the variety of datasets found in cBioPortal, which contain 1000s of samples with as many as 30 clinical variables. Finding gene biomarkers for cancer progression, location or aggressiveness of the tumor may reveal important indicators of diagnosis, treatment, survivability and prognosis of the disease. Likewise, biomarkers obtained from different datasets may differ or coincide due to diverse clinical factors. In this talk, machine learning approaches dealing with the challenges of multiple datasets containing multiple omics and several clinical variables will be discussed. In particular, cases of relevant genes, transcripts and potential protein isoforms associated with prostate cancer progression, aggressiveness and location of the tumor will be presented, along with their relationship to survivability.