TXL-Medical Image and Health Informatics Lab

Ph.D. Student

Xinlu Tang

2020-Present

Data mining
Graph neural networks

School of Biomedical Engineering
Shanghai Jiao Tong University
Room 419, Med-X Research Institute
1954 Hua Shan Road, Shanghai, China

Research Interests

My main research interest revolves around medical data mining and analysis, with a specific focus on front-end feature selection and back-end diagnostic modeling for major diseases. To solve the challenge of limited reliability of artificial intelligence technology in clinical applications, my research work incorporates mathematical modeling, biological apriori, and causal theory.

Brief Biography

Ph.D. student, 2020-Present, School of Biomedical Engineering, Shanghai Jiao Tong University, China.

B.S., 2016-2020, School of Information Science and Technology, University of Science and Technology of China, China.

Current Research Topics

1) Evaluation of Parkinson's disease based on brain imaging
The clinical evaluation of Parkinson's disease depends on the MDS-UPDRS scale, but the evaluation process is complicated and subjective. To address this, we aim to leverage quantitative sensitivity mapping (QSM), a valuable neuroimaging modality, for assistant diagnosis of Parkinson's disease. We expect to develop brain image analysis algorithms that focused on the key issue of stability, enabling the abnormality recognition and symptom evaluation for motor functions in Parkinson's disease.

2) Mining of DNA methylation biomarkers
DNA methylation is closely related to human diseases, making the identification of DNA methylation biomarkers clinically significant. To overcome the challenges posed by confounding factors such as measurement noise and subject-specific characteristics, we are committed to developing effective feature selection algorithms, which integrates biological prior knowledge and mathematical modeling techniques to mine reliable disease-related DNA methylation biomarkers.