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Publications

As of 2020, I have more than 200 publications and presentations in journals and conferences among the top in the fields. I am thankful to my students, fellows and collaborators for making it happen. Below is a list of some of my previous relevant publications.

A more updated list of my publications can be found in the main public scholar databases:

Research Gate Google Scholar DBLP
Luis Rueda Luis Rueda Luis Rueda

Books

  1. L. Rueda. Microarray Image and Data Analysis: Theory and Practice, CRC Press, 2014. More details can be found here.
  2. D. Mery, L. Rueda. Advances in Image and Video Technology. Proceedings of the IEEE Pacific-Rim Symposium on Image and Video Technology (PSIVT 2007). Lecture Notes in Computer Science (LNCS), Vol. 4872, Springer, 2007.
  3. L. Rueda, D. Mery, J. Kittler. Progress in Pattern Recognition, Image Analysis and Applications. Proceedings of the 12th Iberoamerican Congress on Pattern Recognition (CIARP 2007), Lecture Notes in Computer Science (LNCS), Vol. 4756, Springer, 2007.

Book Chapters

  1. A. Alkhateeb, A. Abou Tabl, L. Rueda, Deep Learning in Multi-Omics Data Integration in Cancer Diagnostics. Deep Learning for Biomedical Data Analysis, Springer, 2021.
  2. M. Maleki, M. Hall, L. Rueda, Structural Domains in Prediction of Biological Protein-Protein Interactions. Pattern Recognition in Computational Molecular Biology: Techniques and Approaches, Wiley, 2015, pp. 291-314.
  3. L. Rueda, A. Ali. Introduction to Microarrays. Microarray Image and Data Analysis: Theory and Practice, CRC Press, 2014, pp. 1-40.
  4. I. Rezaeian, L. Rueda. Gridding Methods for DNA Microarray Images. Microarray Image and Data Analysis: Theory and Practice, CRC Press, 2014, pp. 77-108.
  5. D. Rojas, L. Rueda, H. Urrutia, A. Ngom, and G. Carcamo. Automatic Segmentation Methods and Applications to Biofilm Image Analysis. Imaging and Systems, CRC Press, 2011.
  6. N. Subhani, L. Rueda, A. Ngom. On Clustering Gene Expression Time-series Signals. Data Mining in Biomedical Signaling, Imaging and Systems, CRC Press, 2011.

Patents

Journal Publications - selected/most recent

  1. A. Vasighizaker, S. Danda, L. Rueda. Discovering Cell Types Using Manifold Learning and Enhanced Visualization of Single-cell RNA-Seq Data, Scientific Reports 120 (12), 2022, In press. DOI: https://www.nature.com/articles/s41598-021-03613-0.
  2. 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: https://doi.org/10.1109/TCBB.2021.3104734.
  3. A. Neisari, S. Saad, L. Rueda. Spam Review Detection Using Self-organizing Maps and Convolutional Neural Networks, Computers & Security, 2021, In press. DOI: https://doi.org/10.1016/j.cose.2021.102274.
  4. N. Fatima and L. Rueda (2020). iSOM-GSN: An Integrative Approach for Transforming Multi-omic Data into Gene Similarity Networks via Self-organizing Maps, Bioinformatics, 2020. In press.
  5. A. Alkhateeb, G. Atikukke, L. Porter, B.A. Fifield, D. Cavallo-Medved, J. Facca, Y. El-Gohary, T. Zhang, O. Hamzeh, L. Rueda, S. Kanjeekal (2020). Comprehensive targeted gene profiling to determine the genomic signature likely to drive progression of high-grade nonmuscle invasive bladder cancer to muscle invasive bladder cancer, Journal of Clinical Oncology, 38(6_suppl):568-568.
  6. O. Hamzeh, A. Alkhateeb, J. Zheng, S. Kandalam, L. Rueda (2020). Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data. BMC Bioinformatics, 2020, 21,78, 2020. DOI: 10.1186/s12859-020-3345-9.
  7. O. Hamzeh, A. Alkhateeb, J. Zheng, S. Kandalam, C. Leung, G. Atikukke, D. Cavallo-Medved, N. Palanisamy, L. Rueda (2019). A Hierarchical Machine Learning Model to Discover Gleason Grade Group-specific Biomarkers in Prostate Cancer. Diagnostics, 9(4):219.
  8. A. Abou Tabl, A. Alkhateeb, W. El-Maraghy L. Rueda, A. Ngom. A machine learning approach for identifying gene biomarkers guiding the treatment of breast cancer. Frontiers in Genetics, 2019; 10: 256.
  9. A. Alkhateeb, I. Rezaeian, S. Reddy, D. Cavallo-Medved, L. Porter, L. Rueda (2019). "Transcriptomics signature from next-generation sequencing data reveals new transcriptomic biomarkers related to prostate cancer". Cancer Informatics, 18, 1176935119835522.
  10. A. Abou Tabl, A. Alkhateeb, L. Rueda, W. El-Maraghy, A. Ngom (2018). A novel approach for identifying relevant genes for breast cancer survivability on specific therapies. Evolutionary Bioinformatics, 14:1176934318790266. doi: 10.1177/1176934318790266.
  11. S. Peelar, J. Urbanic, B. Hedrick, L. Rueda (2018). Real-Time Visualization of Bead Based Additive Manufacturing Toolpaths Using Implicit Boundary Representations, Computer-Aided Design and Applications, pp. 322-326.
  12. Y. Li, M. Maleki, N. Carruthers, P. Stemmer, A. Ngom, L. Rueda (2018). The predictive performance of short-linear motif features in the prediction of Calmodulin-binding proteins, BMC Bioinformatics, 19 (Suppl 14):410.
  13. F. Firoozbakht, I. Rezaeian, M. D’Agnillo, L. Porter, L. Rueda, A. Ngom (2017). An integrative approach for identifying network biomarkers of breast cancer subtypes using genomics, interactomics and transcriptomics data, Journal of Computational Biology, 24(8):756-766, doi: 10.1089/cmb.2017.0010.
  14. A. Alkhateeb, L. Rueda (2017). Zseq: an approach for preprocessing next generation sequencing data, Journal of Computational Biology, 24(8):746-755, doi: 10.1089/cmb.2017.0021.
  15. E.J. Mucaki, K. Baranova, H.P. Quang, I. Rezaeian, D. Angelov, A. Ngom, L. Rueda, P.K. Rogan (2017). Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Machine Learning. F1000Research, 5:2124. (doi: 10.12688/f1000research.9417.2).
  16. I. Rezaeian, A. Tavakoli, D. Cavallo-Medved, L. Porter, L. Rueda. "A model used to detect differential splice junctions as biomarkers in prostate cancer from RNA-seq data", Journal of Biomedical Informatics, 2016, 60:422-30. doi: 10.1016/j.jbi.2016.03.010.
  17. Y. Li, B. J. Oommen, A. Ngom, L. Rueda. "Pattern Classification Using a New Border Identification Paradigm: The Nearest Border Technique", Neurocomputing, Vol. 157, 2015, pp. 105--117.
  18. I. Rezaeian, L. Rueda. "CMT: A Constrained Multi-level Thresholding Approach for ChIP-Seq Data Analysis", PLoS ONE, 2014, 9(4): e93873. doi:10.1371/journal.pone.0093873.
  19. M. Maleki, M. Hall, L. Rueda. "The Role of Structural Domains in Prediction of Protein-protein Interaction Types". Network Modeling Analysis in Health Informatics and Bioinformatics, 2013, 2(4):267-275. DOI 10.1007/s13721-013-0043-9.
  20. M. Maleki, G. Vasudev, L. Rueda. "The role of electrostatic energies in prediction of obligate protein-protein interactions". Proteome Science, 2013, 11(Suppl 1):S11.
  21. N. Shakiba, L. Rueda. "MicroRNA Identification Using Linear Dimensionality Reduction with Explicit Feature Mapping". BMC Proceedings (Supplements), 2013, 7(Suppl 7):S8.

Conference Publications, Presentations and Posters - most recent

  1. A. Vasighizaker, S. Hora, Y. Trivedi, L. Rueda, “Supervised Cell Type Heterogeneity Detection in Single-cell RNA-seq Data”, International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2022), Gran Canaria, Spain.
  2. S. Hora, A. Vasighizaker, L. Rueda, “SEGCECO: Subgraph Embedding of Gene expression matrix for CEll cell COmmunication prediction”, 26th International Conference on Research in Computational Molecular Biology (RECOMB 2022), San Diego, CA, United States.
  3. A. Vasighizaker, S. Hora, L. Rueda, “A Novel Method to Predict Intercellular Signaling in Single-cell RNA-seq Data via Graph Convolutional Network”, 30th Intelligent Systems for Molecular Biology (ISMB 2022), Madison, Wisconsin, United States.
  4. A. Vasighizaker, Saiteja Danda and Luis Rueda, “Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data”, Highlight Track, The 13th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2022), Chicago, IL.
  5. A. Vasighizaker, Saiteja Danda and Luis Rueda, “Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data", Highlight Paper, 21st European Conference on Computational Biology (ISMB/ECCB 2022), Sitges, Barcelona.
  6. A. Vasighizaker, S. Danda, G. Peralta-Milla, L. Rueda. “Cell Type Identification on Single-cell RNA-Seq Data via Modified Locally Linear Embedding”, 25th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2021), Padua, Italy. Poster presentation, peer-reviewed.
  7. A. Vasighizaker, L. Zhou, L. Rueda. “Cell Type Identification via Convolutional Neural Networks and Self-Organizing Maps on Single-Cell RNA-Seq Data”. Machine Learning Models for Multi-omics Data Integration Workshop (MODI 2021), in conjunction with the 10th ACM Conference on Bioinformatics, Computational Biology (ACM BCB 2021), Virtual, 2021.
  8. Z. Omar, A. Abou Tabl, L. Rueda, W. Elmaraghy. “Identification of Gene Biomarkers for Breast Cancer Lymph Nodes Metastasis using a Deep Neural Network”. Machine Learning Models for Multi-omics Data Integration Workshop (MODI 2021), in conjunction with the 10th ACM Conference on Bioinformatics, Computational Biology (ACM BCB 2021), Virtual, 2021. In press.
  9. A. Vasighizaker, L. Zhou, L. Rueda, “Prediction of Human Pancreas Cell Types via ConvNet on Two-dimensional Mapping of Single-cell RNA-seq Data”, 29th Conference on Intelligent Systems for Molecular Biology - 18th European Conference on Computational Biology (ISMB/ECCB 2021), 2021. Virtual. Poster presentation, peer-reviewed.
  10. S. Danda, A. Vasighizaker, L. Rueda. "Unsupervised Identification of SARS-CoV-2 Target Cell Groups via Nonlinear Dimensionality Reduction on Single-cell RNA-Seq Data," 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South), 2020, pp. 2737-2744, doi: 10.1109/BIBM49941.2020.9313378.
  11. A. Alkhateeb, L. Zhou, A. Abou Tabl, L. Rueda. “Deep Learning Approach for Breast Cancer InClust 5 Prediction based on Multiomics Data Integration”. Machine Learning Models for Multi-omics Data Integration Workshop (MODI), in conjunction with the 10th ACM Conference on Bioinformatics, Computational Biology (ACM BCB), Virtual, 2020, pp. 1-6.
  12. L. Rueda, N. Fatima. “iSOM-GSN: An Integrative Approach for Transforming Multi-omic Data into Gene Similarity Networks via Self-organizing Maps”. 11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2020). Highlight Presentation, Atlanta, USA, 2020, Article No. 38. pp. 1.
  13. G. Atikukke, A. Alkhateeb, L. Porter, B. Fifield, D. Cavallo-Medved, J. Facca, T. Elfiki, A. Elkeilani, L. Rueda, S. Misra. “P-370 Comprehensive targeted genomic profiling and comparative genomic analysis to identify molecular mechanisms driving cancer progression in young-onset sporadic colorectal cancer”, ESMO 22nd World Congress on Gastrointestinal Cancer, 2020, Vol. 31, Suppl. 3, S209-S210.
  14. M. Shah, A. Nour, A. Ngom, L. Rueda. “Cancer Detection Based on Image Classification by Using Convolution Neural Network”, 8th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2020), Granada, Spain, 2020, pp. 275-286.
  15. H. Pham, A. Ngom, L. Rueda, “A Data Integration Approach for Detecting Biomarkers of Breast Cancer Survivability”. 8th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2020), Granada, Spain, 2020, pp. 49-60.
  16. N. Fatima, J. Fernandes, L. Rueda, “Self-organizing Maps Combined with Convolutional Neural Networks Reveal Predictive Gene Similarity Networks on Multi-omics”, 24th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2020), Padua, Italy, 2020. Poster presentation, peer-reviewed.
  17. S. Ozoglu, O. Hamzeh, L. Rueda, “An Integrative Knowledge-based Method to Identify Cancer Biomarkers Based on Gene-Disease Relations”, 24th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2020), Padua, Italy, 2020. Poster presentation, peer-reviewed.
  18. O. Hamzeh, L. Rueda. “A gene-disease-based machine learning approach to identify prostate cancer biomarkers”, Machine Learning Models for Multi-omics Data Integration Workshop (MODI), in conjunction with the 10th ACM Conference on Bioinformatics, Computational Biology (ACM BCB), Niagara Falls, NY, USA, 2019, pages 633-638.
  19. A. Alkhateeb, N. Fatima, L. Rueda, G. Atikukke, S. Misra. “A Deep Learning Model to Identify a Genomic Signature Driving Sporadic Colorectal Cancer in Young Adults”, Machine Learning Models for Multi-omics Data Integration Workshop (MODI), in conjunction with the 10th ACM Conference on Bioinformatics, Computational Biology (ACM BCB), Niagara Falls, NY, USA, 2019, pages 645-645.
  20. O. Hamzeh, L. Rueda. “A multi-modal knowledge-based hybrid feature selection model for identification of cancer biomarkers”. 27th Conference on Intelligent Systems for Molecular Biology - 18th European Conference on Computational Biology (ISMB/ECCB 2019), Basel, Switzerland, 2019. Poster presentation, peer-reviewed.
  21. S. Peelar, J. Urbanic, L. Rueda, B. Hedrick. "Real-Time Visualization of Bead Based Additive Manufacturing Toolpaths Using Implicit Boundary Representations". 15th Annual International CAD Conference (CAD 2018), Paris, France, 2018, pp. 322-326.
  22. O. Hamzeh, T. Zhang, B. Fifield, Y. El-Gohary, R. Goel, T. Deklaj, R. Sorenson, T. Eldson, J. Mathews, A. Ghafoor, G. Atikukke, D. Cavallo-Medved, L. Porter, L. Rueda, S. Kanjeekal. "Muscle invasion in bladder cancer is associated with copy number alterations of TP53, DDR2 and MLL2", 26th Conference on Intelligent Systems for Molecular Biology (ISMB 2018), Chicago, IL, USA, 2018. Poster presentation, peer-reviewed.
  23. H. Pham, J. Guba, L. Porter, L. Rueda, A. Ngom. "A Sub-network Selection Approach for Identifying Biomarkers to Predict Breast Cancer Outcomes", 26th Conference on Intelligent Systems for Molecular Biology (ISMB 2018), Chicago, IL, USA, 2018. Poster presentation, peer-reviewed.
  24. S. Jubair, L. Rueda, A. Ngom. "Identifying subtype specific network-biomarkers of breast cancer survivability", IEEE World Congress on Computational Intelligence/International Joint Conference on Neural Networks (WCCI 2018)/IJCNN 2018), Rio de Janeiro, Brazil, 2018, pp. 1-8.
  25. S. Krishnamoorthy, L. Rueda, S. Saad, H. Elmiligi. "Identification of User Behavioral Biometrics for Authentication using Keystroke Dynamics and Machine Learning", 2nd International Conference on Biometric Engineering and Applications (ICBEA 2018), Amsterdam, The Netherlands, 2018, pp. 50-57.
  26. O. Hamzeh, A. Alkhateeb, L. Rueda. "Predicting Tumor Locations in Prostate Cancer Tissue Using Gene Expression". 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), Granada, Spain, 2018, pp. 343-351.
  27. A. Abou Tabl, A. Alkhateeb, L. Rueda, W. Elmaraghy, A. Ngom. "Identification of the Treatment Survivability Gene Biomarkers of Breast Cancer Patients via a Tree-Based Approach". 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), Granada, Spain, 2018, pp. 166-176.
  28. A. Abou Tabl, A. Alkhateeb, W. El-Maraghy, L. Rueda, A. Ngom. "Identifying Gene Biomarkers for Breast Cancer Survival Using a Tree-based Approach". IEEE Biomedical and Health Informatics (BHI 2018), Las Vegas, NV, 2018. Poster presentation.
  29. R. Razavi-Far, E. Hallaji, M. Saif, L. Rueda. "A Hybrid Scheme for Fault Diagnosis with Partially Labeled Sets of Observations", 16th IEEE International Conference on Machine Learning and Applications (ICMLA 2017), Cancun, Mexico, 2017, pp. 61-67.
  30. H. Quang, L. Rueda, A. Ngom. "Predicting Breast Cancer Outcome under Different Treatments by Feature Selection Approaches", ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB 2017), 2017, Boston, MA, USA, pp. 617-617.
  31. N. Mangalakumar, H. Quang, A. Alkhateeb, L. Rueda, A. Ngom. "Outlier Genes as Biomarkers of Breast Cancer Survivability in Time-Series Data", ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB 2017), 2017, Boston, MA, USA, pp. 594-594.
  32. W. Xu, L. Rueda, A. Ngom. "Drug-Target Interaction Networks Prediction using Short-Linear Motifs", 25th Conference on Intelligent Systems for Molecular Biology and the 16th European Conference on Computational Biology (ISMB/ECCB 2017), Prague, Czech Republic, 2017. Poster presentation, peer-reviewed.
  33. A. Karkar, O. Hamzeh, A. Alkhateeb, L. Rueda, "Finding Biomarkers Associated with Prostate Cancer Gleason Stages using Next Generation Sequencing and Machine Learning Techniques", 25th Conference on Intelligent Systems for Molecular Biology and the 16th European Conference on Computational Biology (ISMB/ECCB 2017), Prague, Czech Republic, 2017. Poster presentation, peer-reviewed.
  34. N. Mangalakumar, A. Alkhateeb, H. Quang, L. Rueda, A. Ngom. "Identifying Gene Biomarkers of Breast Cancer Survivability from Time-Series Data", 25th Conference on Intelligent Systems for Molecular Biology and the 16th European Conference on Computational Biology (ISMB/ECCB 2017), Prague, Czech Republic, 2017. Poster presentation, peer-reviewed.
  35. H. Ahmed, O. Hamzeh, A. Alkhateeb, L. Rueda. "An Open Source Machine Learning Tool for Identifying Biomarkers in Next Generation Sequencing", The Great Lakes Bioinformatics Conference (GLBIO 2017), 2017, Chicago, IL, USA. Poster presentation.
  36. S. Chakrabarti, R. Razavi-Far, M. Saif, L. Rueda. "'Multi-Class Heteroscedastic Linear Dimensionality Reduction Scheme for Diagnosing Process Faults", 30th Canadian IEEE Conference on Electrical and Computer Engineering (CCECE 2017), Windsor, ON, Canada, 2017.
  37. O. Hamzeh, A. Alkhateeb, I. Rezaeian, A. Karkar, L. Rueda. "Finding Transcripts Associated with Prostate Cancer Gleason Stages Using Next Generation Sequencing and Machine Learning Techniques". 5th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2017), Granada, Spain, 2017, pp. 337-348.
  38. Y. Li, M. Maleki, N.J. Carruthers, L. Rueda, P.M. Stemmer, A. Ngom. "Prediction of Calmodulin Binding Proteins Using Short Linear Motifs". 5th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2017), Granada, Spain, 2017, pp. 107-117.
  39. R. Etemadi, A. Alkhateeb, I. Rezaeian, L. Rueda, "Identification of Discriminative Genes for Predicting Breast Cancer Subtypes", Workshop on Health Informatics and Data Science (HI-DS 2016) - IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2016), Shenzhen, China, 2016, pp: 1184-1188.
  40. H. Quang, A. Ngom, L. Rueda, "A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification", IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2016), Shenzhen, China, 2016, pp. 1535-1541.
  41. H. Quang, I. Rezaeian, E. Mucaki, A. Ngom, L. Rueda, P. Rogan. "Predicting Breast Cancer Drug Response via an Apriori-like Gene Selection Approach", The Fourth International Society for Computational Biology Latin America Bioinformatics Conference (ISCB-LA 2016), Buenos Aires, Argentina. Poster presentation.
  42. M. Alsheri, A. Alkhateeb, I. Rezaeian, L. Rueda. "Discovery of Protein Isoforms for Different Stages of Prostate Cancer", The Fourth International Society for Computational Biology Latin America Bioinformatics Conference (ISCB-LA 2016), Buenos Aires, Argentina. Oral presentation.
  43. H. Pham, A. Ngom, L. Rueda, "AFSP -- an efficient method for classifier-specific feature selection", IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athens, Greece, 2016, pp. 1-8.
  44. M. Alshehri, I. Rezaeian, A. Alkhateeb, L. Rueda. "A Machine Learning Model for Discovery of Protein Isoforms as Biomarkers", ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACMBCB 2016), Seattle, WA, USA, 2016, pp. 474-475.
  45. F. Firoozbakht, I. Rezaeian, A. Ngom, L. Rueda, "An Integrative Approach for Identification of Network Biomarkers in Breast Cancer Subtypes", 12th International Symposium on Bioinformatics Research and Applications (ISBRA 2016), Minsk, Belarus, 2016, pp. 1-4.
  46. M. Alshehri, A. Alkhateeb, I. Rezaeian, L. Rueda, "Potential Protein Isoforms Reveal Additional Information on Biomarkers Obtained from RNA-Seq Data", 24th Conference on Intelligent Systems for Molecular Biology (ISMB 2016), Orlando, Florida, USA, 2016. Poster presentation, peer-reviewed.
  47. B. Elkarami, A. Alkhateeb, L. Rueda, "Cost-Sensitive Classification on Class-balanced Ensembles for Imbalanced Non-coding RNA Data", The IEEE Engineering in Medicine and Biology Society (EMBS) International Student Conference (ISC) 2016, Ottawa, Canada, pp. 1-4.



Ⓒ Luis Rueda, 2022