송길태 연구실

Generative AI produces data with patterns and structures similar to the input data. We leverage these models for creating medical documentation. Additionally, we develop Large Language Models for biomarker discovery using various genomic data types.

We develop graph-based deep learning models to discover novel biomarkers using various genomic resources. Additionally, we construct disease risk prediction models based on multi-modal data from patients.

We focus on developing strategies to enhance the performance of machine learning models for tabular data, including supervised learning and data imputation. These models are integrated as reliable and interpretable AI systems.

We develop software pipelines for integrating various massive datasets. Visualization is important to help users make decisions. We design efficient ways for showing important features and their correlations learned from data.