5 papers
Missing-Modality-Aware Graph Neural Network for Cancer Classification
Sina Tabakhi, Chen, Haiping Lu
A key challenge in learning from multimodal biological data is missing modalities, where data from one or more modalities are absent for some patients. Existing approaches either e…
Co-Evolution-Based Metal-Binding Residue Prediction with Graph Neural Networks
Sayedmohammadreza Rastegari, Sina Tabakhi, Xianyuan Liu +3
Understanding protein-metal interactions is central to structural biology, with metal ions being vital for catalysis, stability, and signal transduction. Predicting metal-binding r…
Towards deployment-centric multimodal AI beyond vision and language
Xianyuan Liu, Jiayang Zhang, Shuo Zhou +45
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as h…
Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives
Xiaokun Liu, Sayedmohammadreza Rastegari, Yijun Huang +12
In cancer therapeutics, protein-metal binding mechanisms critically govern the pharmacokinetics and targeting efficacy of drugs, thereby fundamentally shaping the rational design o…
Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning
Jiayang Zhang, Xianyuan Liu, Wei Wu +6
Virus-like particles (VLPs) are valuable for vaccine development due to their immune-triggering properties. Understanding their stoichiometry, the number of protein subunits to for…