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Dr. Luis Rueda

Professor
519-253-3000 ext. 3793
lrueda@uwindsor.ca
Office: 8110 LT

Education:

  • B.Sc. in Computer Science (Informatics), National University of San Juan, Argentina, 1993
  • M.Sc. in Computer Science, Carleton University, Canada, 1998
  • Ph.D. in Computer Science, Carleton University, Canada, 2002

Professional Affiliations:

  • Senior Member of the Institute of Electrical and Electronics Engineers (IEEE)
  • Member of the Association for Computing Machinery (ACM)
  • Member of the International Association of Pattern Recognition (IAPR)
  • Member of the International Society for Computational Biology (ISCB)

 

Dr. Luis Rueda

Research Interests:

  • Machine learning, pattern recognition and deep learning
  • Computational biology and bioinformatics
  • Transcriptomic and Interactomic Data Analytics
  • Biological knowledge discovery and cancer biomarkers
  • Data security and user authentication

Selected Publications:

  • Abou Tabl, A. Alkhateeb, L. Rueda, W. El-Maraghy, A. Ngom. “A novel approach for identifying relevant genes for breast cancer survivability on specific therapies”, Evolutionary Bioinformatics, 2018, 14:1176934318790266. doi: 10.1177/1176934318790266.
  • Y. Li, M. Maleki, N. Carruthers, P. Stemmer, A. Ngom, L. Rueda. “The predictive performance of short-linear motif features in the prediction of Calmodulin-binding proteins”, BMC Bioinformatics, 2018. In press.
  • F. Firoozbakht, I. Rezaeian, M. D’Agnillo, L. Porter, L. Rueda, A. Ngom. “An integrative approach for identifying network biomarkers of breast cancer subtypes using genomics, interactomics and transcriptomics data”, Journal of Computational Biology, 2017, 24(8):756-766, doi: 10.1089/cmb.2017.0010.
  • Alkhateeb, L. Rueda. “Zseq : an approach for preprocessing next generation sequencing data”, Journal of Computational Biology, 2017, 24(8):746-755, doi: 10.1089/cmb.2017.0021.
  • E.J. Mucaki, K. Baranova, H.P. Quang, I. Rezaeian, D. Angelov, A. Ngom, L. Rueda, P.K. Rogan. “Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Machine Learning”. F1000Research, 2017, 5:2124. (doi: 10.12688/f1000research.9417.2)

Website: