361 citations · 786 across the 19 of their papers we have counts for
19 papers
RAFT-UP: Robust Alignment for Spatial Transcriptomics with Explicit Control of Spatial Distortion
Yaqi Wu, Jingfeng Wang, Xin Maizie Zhou +2
Spatial transcriptomics (ST) profiles gene expression across a tissue section while preserving the spatial coordinates. Because current ST technologies typically profile two-dimens…
Synchronization of Unbalanced Dynamical Optimal Transport across Multiple Spaces
Zixuan Cang, Jingfeng Wang, Xiaoqi Wei +1
Many biological systems are observed through heterogeneous modalities, requiring transport models that couple dynamics across spaces while allowing mass variation. To address this…
PHD-MS: Multiscale Domain Identification for Spatial Transcriptomics via Persistent Homology
Perry Beamer, Zixuan Cang
Spatial transcriptomics (ST) measures gene expression at a set of spatial locations in a tissue. Communities of nearby cells that express similar genes form \textit{spatial domains…
OTMol: Robust Molecular Structure Comparison via Optimal Transport
Xiaoqi Wei, Xuhang Dai, Yaqi Wu +3
Root-mean-square deviation (RMSD) is widely used to assess structural similarity in systems ranging from flexible ligand conformers to complex molecular cluster configurations. Des…
Synchronized Optimal Transport for Joint Modeling of Dynamics Across Multiple Spaces
Zixuan Cang, Yanxiang Zhao
Optimal transport has been an essential tool for reconstructing dynamics from complex data. With the increasingly available multifaceted data, a system can often be characterized a…
Supervised Gromov-Wasserstein Optimal Transport
Zixuan Cang, Yaqi Wu, Yanxiang Zhao
We introduce the supervised Gromov-Wasserstein (sGW) optimal transport, an extension of Gromov-Wasserstein by incorporating potential infinity patterns in the cost tensor. sGW enab…
Poisson-Boltzmann based machine learning (PBML) model for electrostatic analysis
Jiahui Chen, Yongjia Xu, Xin Yang +3
Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it…
Topological and geometric analysis of cell states in single-cell transcriptomic data
Tram Huynh, Zixuan Cang
Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been d…
Supervised Optimal Transport
Zixuan Cang, Qing Nie, Yanxiang Zhao
Optimal Transport, a theory for optimal allocation of resources, is widely used in various fields such as astrophysics, machine learning, and imaging science. However, many applica…
AVIDA: Alternating method for Visualizing and Integrating Data
Kathryn Dover, Zixuan Cang, Anna Ma +2
High-dimensional multimodal data arises in many scientific fields. The integration of multimodal data becomes challenging when there is no known correspondence between the samples…
A review of mathematical representations of biomolecules
Duc D Nguyen, Zixuan Cang, Guo-Wei Wei
Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Predictio…
Persistent cohomology for data with multicomponent heterogeneous information
Zixuan Cang, Guo-Wei Wei
Persistent homology is a powerful tool for characterizing the topology of a data set at various geometric scales. When applied to the description of molecular structures, persisten…
Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges
Duc Duy Nguyen, Zixuan Cang, Kedi Wu +3
Advanced mathematics, such as multiscale weighted colored graph and element specific persistent homology, and machine learning including deep neural networks were integrated to con…
Evolutionary homology on coupled dynamical systems
Zixuan Cang, Elizabeth Munch, Guo-Wei Wei
Time dependence is a universal phenomenon in nature, and a variety of mathematical models in terms of dynamical systems have been developed to understand the time-dependent behavio…
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
Zixuan Cang, Lin Mu, Guowei Wei
This work introduces a number of algebraic topology approaches, such as multicomponent persistent homology, multi-level persistent homology and electrostatic persistence for the re…
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Zixuan Cang, Guo-Wei Wei
Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional…
Topological fingerprints reveal protein-ligand binding mechanism
Zixuan Cang, Guo-Wei Wei
Protein-ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme constructio…
Analysis and prediction of protein folding energy changes upon mutation by element specific persistent homology
Zixuan Cang, Guo-Wei Wei
Motivation: Site directed mutagenesis is widely used to understand the structure and function of biomolecules. Computational prediction of protein mutation impacts offers a fast, e…
A topological approach for protein classification
Zixuan Cang, Lin Mu, Kedi Wu +3
Protein function and dynamics are closely related to its sequence and structure. However prediction of protein function and dynamics from its sequence and structure is still a fund…