7 papers
Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives
Xianyuan Liu, Charles Anjah, Benjamin E. Jolly +9
Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel…
Benchmarking bandgap prediction in semiconductors under experimental and realistic evaluation settings
Haolin Wang, Xianyuan Liu, Anna Jungbluth +3
Accurate bandgap prediction is crucial for semiconductor applications, yet machine learning models trained on computational data often struggle to generalize to experimental bandga…
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…
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…
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…
Mask prior-guided denoising diffusion improves inverse protein folding
Peizhen Bai, Filip Miljković, Xianyuan Liu +4
Inverse protein folding generates valid amino acid sequences that can fold into a desired protein structure, with recent deep-learning advances showing strong potential and competi…
Heterogeneous graph attention network improves cancer multiomics integration
Sina Tabakhi, Charlotte Vandermeulen, Ian Sudbery +1
The increase in high-dimensional multiomics data demands advanced integration models to capture the complexity of human diseases. Graph-based deep learning integration models, desp…