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)
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:
1. A. Vasighizaker, S. Hora, R. Zheng, L. Rueda. “SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell Communication.” Briefings in Bioinformatics, 2024, 25(3):bbae160.
2. S. Modak, E. Abdel-Raheem, L. Rueda. “GPD-Nodule: A Lightweight Lung Nodule Detection and Segmentation Framework on Computed Tomography Images Using Uniform Superpixel Generation”. IEEE Access. 2024, pp. 154933-154948.
3. A. Vasighizaker, Y. Trivedi, L. Rueda. “Cell Type Annotation Model Selection: General-Purpose vs. Pattern-Aware Feature Gene Selection in Single-Cell RNA-Seq Data.” Genes, 2023, 14(3):596. DOI: 10.3390/genes14030596.
4. S. Modak, E. Abdel-Raheem, L. Rueda. “Applications of deep learning in disease diagnosis of chest radiographs: A survey on materials and methods.” Biomedical Engineering Advances, 2023, 5(2023):100076, DOI:10.1016/j.bea.2023.100076.
5. A. Vasighizaker, S. Danda, L. Rueda. “Discovering Cell Types Using Manifold Learning and Enhanced Visualization of Single-cell RNA-Seq Data”. Scientific Reports, Sci Rep 12, 120, 2022. DOI: 10.1038/s41598-021-03613-0.
6. M. Naik, L. Rueda, A. Vasighizaker. “Identification of Enriched Regions in ChIP-seq Data via a Linear-time Multi-level Thresholding Algorithm”. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, DOI: 10.1109/TCBB.2021.3104734.
7. N. Fatima, L. Rueda. “iSOM-GSN: An Integrative Approach for Transforming Multi-omic Data into Gene Similarity Networks via Self-organizing Maps”, Bioinformatics, Volume 36, Issue 15, 2020, Pages 4248–4254.