Professor Luis Rueda

1. Spatial Single-Cell Transcriptomics and Multi-Omics

• Ligand-receptor dynamics in heterophily-aware graph neural networks for enhanced cell type prediction from single-cell RNA-seq data (2025). Frontiers in Molecular Biosciences.
• PPPCT: Privacy-Preserving framework for Parallel Clustering Transcriptomics data (2024). Computers in Biology and Medicine.
• Cell type annotation model selection: general-purpose vs. pattern-aware feature gene selection in single-cell RNA-seq data (2023). Genes.
• Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data (2022). Scientific Reports.
• Clustering Microarray Time-Series Data Using a Mean-Square-Error Profile Alignment Algorithm (2009). Bioinformatics Journal.

• Heterophily-Aware Hypergraph Neural Networks for Cell Type Prediction Using Ligand-Receptor-Informed Single-Cell RNA-Seq Data (2025). IEEE BIBM.
• HeteroGraphNet: A Ligand-Receptor Informed, Heterophily-Adapted Graph Neural Network for Cell Type Prediction in scRNA-Seq Data (2025). IEEE BIBM.
• Comparative Analysis of Supervised Cell Type Detection in Single-Cell RNA-seq Data (2022). International Work-Conference on Bioinformatics and Biomedical Engineering.
• DeepSLIM: a deep learning approach to identify predictive short-linear motifs for protein sequence classification (2022). IEEE CIBCB.
• Cell type identification via convolutional neural networks and self-organizing maps on single-cell RNA-seq data (2021). Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics.

2. Cell-Cell Communication and Molecular Interactions

• Ligand-receptor dynamics in heterophily-aware graph neural networks for enhanced cell type prediction from single-cell RNA-seq data (2025). Frontiers in Molecular Biosciences.
• MolPACL: Molecular Property Prediction Based on Prompt Augmentation and Contrastive Learning (2026). IEEE/ACM Trans in Computational Biology and Bioinformatics
• A survey of contrastive learning methods in molecular representation (2026). Briefings in Bioinformatics.
• PPISHES-an enhanced physicochemical approach for predicting protein interaction sites using graph neural networks (2025). Protein Science.
• SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CElI-cell Communication (2024). Briefings in Bioinformatics.

• A standalone tool for finding ORFs and reconstructing potential protein isoforms from RNA-Seq data. FIOOO research(Poster).
• Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium. FIOOO research(Poster).

3. Biomedical Imaging and Image Analysis

• EGPD-Nodule: An enhanced lung nodule detection and segmentation framework utilizing superpixels and graph neural networks (2026). Signal, Image and Video Processing.
• Gpd-nodule: A lightweight lung nodule detection and segmentation framework on computed tomography images using uniform superpixel generation (2024). IEEE Access.
• Applications of deep learning in disease diagnosis of chest radiographs: A survey on materials and methods (2023). Biomedical Engineering Advances.

• Delaunay Triangulations: A New Avenue for Classification of Biomedical Images Using Graph Neural Networks (2025). IEEE BIBE(2025).
• Integrating Point Cloud Generation and Graph Machine Learning to Reduce False Positives in Pulmonary Nodule Detection (2025). IEEE BioCAS.
• Harnessing the power of graph propagation in lung nodule detection (2024). International Conference on Artificial Intelligence in Medicine.
• Fusing superpixel graph propagation and positional convolutions for small object detection in computed tomography scan (2024). International Conference on Microelectronics (ICM).
• Lung nodule segmentation on CT scan images using Patchwise Iterative Graph Clustering (2023). IEEE ISCAS.
• Examining the performance of melanoma classification using superpixel segmentation: A comparative analysis (2023). International Conference on Microelectronics (ICM).

• Fast Graph Neural Network for Image Classification (2025). arXiv preprint arXiv:2508.14958.
• Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams (2025). arXiv preprint arXiv:2508.14218.

4. Recommender Systems

• End-to-End Personalization via Unifying LLM Agents and Graph Attention Networks for Entertainment Recommendation (2026). Information.
• Spam review detection using self-organizing maps and convolutional neural networks (2021). Computers & Security.

• Lusifer: LLM-based user simulated feedback environment for online recommender systems (2025). IEEE ICMI(2025).
• Identification of User Behavioral Biometrics for Authentication Using Keystroke Dynamics and Machine Learning (2018). 2018 2nd International Conference on Biometric Engineering and Applications.

• Vectorized Context-Aware Embeddings for GAT-Based Collaborative Filtering (2025). arXiv preprint arXiv:2510.26461.
• End-to-End Personalization: Unifying Recommender Systems with Large Language Models (2025). arXiv preprint arXiv:2508.01514.

5. Hypergraph Neural Networks

• Heterophily-Aware Hypergraph Neural Networks for Cell Type Prediction Using Ligand-Receptor-Informed Single-Cell RNA-Seq Data (2025). IEEE BIBM(2025).

• Hypergraph neural networks reveal spatial domains from single-cell transcriptomics data (2025). bioRxiv.
• CTODS: Polynomial-Time Construction of Hypergraphs via Constrained Overlapping Densest Subgraphs for Enhanced Neural Network Performance. arXiv.

6. Associative Memory Models

• Hierarchical Hopfield Network Decomposition: A Spiked Covariance Framework for Latent Prototype Discovery. NFAM Workshop.

• Improving Out-of-Distribution Data Handling and Corruption Resistance via Modern Hopfield Networks (2024). International Conference on Pattern Recognition, ICPR 2024.

7. Cancer Research and Clinical Disease Diagnostics

• Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer (2019). Cancer Informatics.
• su1134 targeted gene sequencing of sporadic young-onset colon cancer samples using trusight oncology 500 from illumina identifies recurrent mutations in ddr2 oncogene (2024). Gastroenterology.
• A fecal-microbial-extracellular-vesicles-based metabolomics machine learning framework and biomarker discovery for predicting colorectal cancer patients (2023). Metabolites.
• Computationally repurposing drugs for breast cancer subtypes using a network-based approach (2022). BMC Bioinformatics.
• Deep learning in multi-omics data integration in cancer diagnostic (2021). Deep Learning for Biomedical Data Analysis.
• The circadian clock gene, Bmal1, regulates intestinal stem cell signaling and represses tumor initiation (2021). Cellular and Molecular Gastroenterology and Hepatology.
• A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer (2019). Frontiers in Genetics.

• Comparative genomic analysis to identify a signature of sporadic colorectal cancer development in young adults (2024). American Society of Clinical Oncology.
• Comprehensive gene sequencing to identify progression predictors to muscle-invasive bladder cancer (2023). American Society of Clinical Oncology.