1 citations · 2 across the 8 of their papers we have counts for
9 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
The fundamental relationship among protein sequence, structure, function, and physicochemical properties is a central principle in biology. While in principle protein function and…
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,…
Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data
Hongsong Feng, Sean Cottrell, Yuta Hozumi +1
Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology, offering unparalleled insights into the intricate landscape of cellular diversity and gene expres…
Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization
Yuta Hozumi, Guo-Wei Wei
Single-cell RNA sequencing (scRNA-seq) is a relatively new technology that has stimulated enormous interest in statistics, data science, and computational biology due to the high d…
K-Nearest-Neighbors Induced Topological PCA for scRNA Sequence Data Analysis
Sean Cottrell, Yuta Hozumi, Guo-Wei Wei
Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell-cell communication, cell differentiation, and differen…
Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE
Yuta Hozumi, Gu-Wei Wei
Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell-cell communication, cell differentiation, and differen…