60 citations · 63 across the 3 of their papers we have counts for
5 papers
Assay-Aware BindingDB: Curating Experimental Context for Binding Affinity Prediction
Ming-Hsiu Wu, Xuejiao Shirley Guo, Bingsong Zeng +5
Protein--ligand binding affinity prediction is fundamental to computational drug discovery, yet modern AI-driven models are limited by pervasive heterogeneity in their training dat…
Towards Precision Protein-Ligand Affinity Prediction Benchmark: A Complete and Modification-Aware DAVIS Dataset
Ming-Hsiu Wu, Ziqian Xie, Shuiwang Ji +1
Advancements in AI for science unlocks capabilities for critical drug discovery tasks such as protein-ligand binding affinity prediction. However, current models overfit to existin…
Language Models for Controllable DNA Sequence Design
Xingyu Su, Xiner Li, Yuchao Lin +3
We consider controllable DNA sequence design, where sequences are generated by conditioning on specific biological properties. While language models (LMs) such as GPT and BERT have…
Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies
Yaochen Xie, Ziqian Xie, Sheikh Muhammad Saiful Islam +2
Genome-wide association studies (GWAS) are used to identify relationships between genetic variations and specific traits. When applied to high-dimensional medical imaging data, a k…
Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction
Laila Rasmy, Yang Xiang, Ziqian Xie +2
Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often re…