Guowei Wei

Professor

Michigan State University

United States

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Guowei Wei is a Professor at Michigan State University in the United States. His research areas include machine learning, topological data analysis, and the application of artificial intelligence in drug discovery. Recent articles have explored topics such as chaotic predictions, genomic sequence analysis, and energy predictions in multi-atom systems.

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Collaborators (4)

Min Su

University of Missouri

UNITED STATES

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Daniel Woldring

Assistant Professor

Michigan State University

UNITED STATES

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Yuta Hozumi

Hale Visiting Assistant Professor

Georgia Institute of Technology

UNITED STATES

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Jiahui Chen

Assistant Professsor

University of Arkansas at Fayetteville

UNITED STATES

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Guowei Wei

Research Interests

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About

Articles (29)

Machine learning predictions from unpredictable chaos

Topological sequence analysis of genomes: category theory approaches

Khovanov Laplacian and Khovanov Dirac for knots and links

Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning

Persistent Topological Laplacians—A Survey

A review of machine learning methods for imbalanced data challenges in chemistry

Artificial intelligence approaches for anti-addiction drug discovery

Category-specific topological learning of metal–organic frameworks

Enhancing energy predictions in multi-atom systems with multiscale topological learning

Preventing future zoonosis: SARS-CoV-2 mutations enhance human–animal cross-transmission

Mayer-homology learning prediction of protein-ligand binding affinities

Graph-Based Bidirectional Transformer Decision Threshold Adjustment Algorithm for Class-Imbalanced Molecular Data

Transformer technology in molecular science

Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions

K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis

Combinatorial and Hodge Laplacians: Similarities and Differences

Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data

Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation

Analyzing scRNA-seq data by CCP-assisted UMAP and tSNE

Topological deep learning based deep mutational scanning

Persistent Topological Laplacian Analysis of SARS-CoV-2 Variants

Machine-learning repurposing of DrugBank compounds for opioid use disorder

Machine-learning analysis of opioid use disorder informed by MOR, DOR, KOR, NOR and ZOR-based interactome networks

Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models

Structural insights into the elevator-type transport mechanism of a bacterial ZIP metal transporter

Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants

Charge substitutions at the voltage-sensing module of domain III enhance actions of site-3 and site-4 toxins on an insect sodium channel

Algebraic graph-assisted bidirectional transformers for molecular property prediction

Author Correction: Analysis of SARS-CoV-2 mutations in the United States suggests presence of four substrains and novel variants

Collaborators (4)

Min Su

Daniel Woldring

Yuta Hozumi

Jiahui Chen

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