Sa Li

I am a Ph.D. candidate at Oakland University working on graph neural networks for cancer driver gene identification. My research focuses on topology-adaptive GNNs, multi-omics data integration, and interpretable deep learning for biologically grounded discovery.

Research Interests

Graph Neural Networks · Machine Learning · Deep Learning· Computational Cancer Genomics · Interpretable AI

Research Project & Funding

This research is conducted under the supervision of Dr. Tianle Ma and supported by the U.S. National Science Foundation (NSF) under Grant No. 2245805. The project focuses on developing topology-adaptive and interpretable graph neural networks for cancer driver gene identification through large-scale multi-omics data integration and biological network modeling.

The proposed research advances foundational methods in graph representation learning, computational cancer genomics, and explainable artificial intelligence, with the goal of enabling biologically grounded discovery and improving the interpretability of deep learning models in precision medicine.

Teaching Experience

Teaching Assistant
Oakland University, Rochester, MI, United States

• Winter 2023
  CSI 2470: Introduction to Computer Networks;
  CSI 2300: Object-Oriented Computing (Java, JavaFX for GUI design)
• Fall 2022
  CSI 2470: Introduction to Computer Networks;
  CSI 2300: Object-Oriented Computing
• Winter 2022
  CSI 2300: Object-Oriented Computing;
  CSI 4350: Programming Languages
• Fall 2021
  CSI 3430: Theory of Computation;
  CSI 5720: Software Security
• Winter 2021
  CSI 2440: Computer Systems;
  CSI 5200: Fundamentals / Software Modeling

Instructor
Southwestern Michigan College, Dowagiac, MI, United States  |  Fall 2021
ISYS–115: Introduction to Programming Logic and Design

Publications

• Sa, L., Ma, T.
  SGL-CDP: Spectral Graph Learning for Cancer Driver Prioritization.
  IEEE Access, 2026.
  DOI: 10.1109/ACCESS.2026.3675370
• Sa, L., Ma, T.
  Pathway Representation Learning for Interpretable Graph Neural Networks.
  IEEE Access, 2026.
  DOI: 10.1109/ACCESS.2026.3673117
• Sa, L., Shader, J., Bhattacharya, A., Ma, T.
  Integration of Multi-Omics Data with Topology Adaptive Graph Convolutional Network for Cancer Driver Gene Identification.
  IEEE Transactions on Computational Biology and Bioinformatics,
  Early Access, November 27, 2025.
  DOI: 10.1109/TCBBIO.2025.3636976
  PMID: 41308109
• Sa, L., Shader, J., Bhattacharya, A., Ma, T.
  Graph Neural Network with Pretrained Embeddings for Cancer and miRNA Regulator Discovery.
  Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB 2025).
  Article No. 78, October 12, 2025.
  DOI: 10.1145/3768322.3769029
• Sa, L., Shader, J., Ma, T.
  PERGAT: Pretrained Embeddings of Graph Neural Networks for miRNA–Cancer Association Prediction.
  Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024), Lisbon, Portugal, 2024.
  DOI: 10.1109/BIBM62325.2024.10822135

Manuscripts Under Review

• Sa, L., Ma, T.
  Pathway-Informed Spectral Embeddings for Explainable Cancer Genomics.
  Submitted to BMC Bioinformatics, under review.
• Sa, L., Ma, T.
  Learning Interpretable Gene Representations with Adaptive Chebyshev Graph Neural Networks.
  Submitted to Artificial Intelligence in Medicine, under review.
• Sa, L., Ma, T.
  Multi-Omics Integration with Spectral Graph Neural Networks for Cancer Driver Gene Identification.
  Submitted to IEEE Transactions on Computational Biology and Bioinformatics, under review.

Presentations

• Sa, L.
  GKGL-PE: A GNN-based Knowledge Graph Learning Framework for Pathway Embeddings.
  Presented at the International Conference on Intelligent Biology and Medicine (ICIBM 2024), Houston, TX, USA, October 10–12, 2024.
• Sa, L.
  Adaptive Chebyshev Graph Neural Network for Cancer Gene Prediction with Multi-Omics Integration.
  Presented at the International Conference on Intelligent Biology and Medicine (ICIBM 2025), Columbus, OH, USA, August 3–5, 2025.

Hobbies

AlphaGo · Basketball · Skateboarding · Acoustic Guitar