From the 1 of 6 linked papers with an AI index.
6 papers
Persistent Manifold Learning of Protein Properties
Xingjian Xu, Zhe Su, Guo-Wei Wei +1
Predicting how tightly two biomolecules bind remains a major challenge, in part because different interaction classes present dissimilar interfaces, from compact metal-coordinated…
AlphaFunctor: Bridging The Gap Between Protein Function Annotation and Property Prediction
Xiang Liu, Anna E. Yee, Josh V. Vermaas +2
AlphaFunctor is a foundation‑model style platform that predicts protein functions (Gene Ontology terms) directly from sequence and then maps those functions to various protein prop…
Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation
Xiang Liu, Junjie Wee, Guo-Wei Wei
Understanding how protein mutations affect protein-nucleic acid binding is critical for unraveling disease mechanisms and advancing therapies. Current experimental approaches are l…
Machine learning predictions from unpredictable chaos
Jian Jiang, Long Chen, Lu ke +7
Chaos is omnipresent in nature, and its understanding provides enormous social and economic benefits. However, the unpredictability of chaotic systems is a textbook concept due to…
Revealing the Shape of Genome Space via K-mer Topology
Yuta Hozumi, Guo-Wei Wei
Despite decades of effort, understanding the shape of genome space in biology remains a challenge due to the similarity, variability, diversity, and plasticity of evolutionary rela…
Persistent de Rham-Hodge Laplacians in Eulerian representation for manifold topological learning
Zhe Su, Yiying Tong, Guo-Wei Wei
Recently, topological data analysis has become a trending topic in data science and engineering. However, the key technique of topological data analysis, i.e., persistent homology,…