5 papers
DoAtlas-2: A Foundation for Self-Evolving Causal Biomedical Discovery
Yulong Li, Rong Xia, Yuxuan Zhang +12
We introduce DoAtlas-2, a foundation for self-evolving causal biomedical discovery that organizes knowledge around causal mechanisms and advances through external evidence from hum…
DoAtlas-1: A Causal Compilation Paradigm for Clinical AI
Yulong Li, Jianxu Chen, Xiwei Liu +9
Medical foundation models generate narrative explanations but cannot quantify intervention effects, detect evidence conflicts, or validate literature claims, limiting clinical audi…
Few-Label Multimodal Modeling of SNP Variants and ECG Phenotypes Using Large Language Models for Cardiovascular Risk Stratification
Niranjana Arun Menon, Yulong Li, Iqra Farooq +3
Cardiovascular disease (CVD) risk stratification remains a major challenge due to its multifactorial nature and limited availability of high-quality labeled datasets. While genomic…
A Machine Learning Approach to Predict Biological Age and its Longitudinal Drivers
Nazira Dunbayeva, Yulong Li, Yutong Xie +1
Predicting an individual's aging trajectory is a central challenge in preventative medicine and bioinformatics. While machine learning models can predict chronological age from bio…
How Effectively Can Large Language Models Connect SNP Variants and ECG Phenotypes for Cardiovascular Risk Prediction?
Niranjana Arun Menon, Iqra Farooq, Yulong Li +4
Cardiovascular disease (CVD) prediction remains a tremendous challenge due to its multifactorial etiology and global burden of morbidity and mortality. Despite the growing availabi…