Zobrazeno 1 - 10
of 107
pro vyhledávání: '"Cang, Zixuan"'
Autor:
Cang, Zixuan, Zhao, Yanxiang
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 across multiple spaces. Therefore, it is crucial to maintain coherence
Externí odkaz:
http://arxiv.org/abs/2406.03319
We introduce the supervised Gromov-Wasserstein (sGW) optimal transport, an extension of Gromov-Wasserstein by incorporating potential infinity patterns in the cost tensor. sGW enables the enforcement of application-induced constraints such as the pre
Externí odkaz:
http://arxiv.org/abs/2401.06266
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 is highly challenging to compute accurate PB electrostatic solvation
Externí odkaz:
http://arxiv.org/abs/2312.11482
Autor:
Huynh, Tram, Cang, Zixuan
Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data where the
Externí odkaz:
http://arxiv.org/abs/2309.07950
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 applications impose elementwise constraints on the transport plan which tradi
Externí odkaz:
http://arxiv.org/abs/2206.13410
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 and the features of different datasets. To tackle this challenge, we i
Externí odkaz:
http://arxiv.org/abs/2206.00135
Publikováno v:
In Biophysical Journal February 2024
Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges. However
Externí odkaz:
http://arxiv.org/abs/1912.04724
Akademický článek
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Autor:
Cang, Zixuan, Wei, Guo-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, persistent homology can capture the multiscale geometric features and reveal ce
Externí odkaz:
http://arxiv.org/abs/1807.11120