Match tier Likely match
Presence Current · Arkansas
Last published 2025
Sources OpenAlex · ORCID
Refreshed 2026-08-15

Weigong Ge

This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.

High Impact

Researcher

Also affiliated: United States Food and Drug Administration (2006–2025); Northern Kentucky University (2021); Central China Normal University (2021); Cancer Research And Biostatistics (2025); Food and Drug Administration (2010); ICF International (United States) (2008); Thermo Fisher Scientific (Norway) (2014)

Faculty Researcher

25 h-index 72 pubs 6,831 cited

  • Humans
  • Polymorphism, Single Nucleotide
  • Animals
  • Algorithms
  • Oligonucleotide Array Sequence Analysis
  • Environmental Pollutants
  • Genome, Human
  • Analgesics, Opioid
  • Software
  • Rats
  • Data Mining
  • Endocrine Disruptors
  • Machine Learning
  • Reproducibility of Results
  • Gene Expression Profiling

Biography and Research Information

OverviewAI-generated summary

Weigong Ge's research program focuses on the application of computational methods, including machine learning and deep learning, to address challenges in toxicology, drug discovery, and bioinformatics. His work has investigated the prediction of molecular properties relevant to drug safety, such as the blockade of the hERG channel, utilizing quantitative structure-activity relationship (QSAR) modeling. Ge has also explored the use of AI for analyzing large biomedical datasets, including the FAERS database for opioid-related adverse events and comparing topic modeling algorithms for risk assessment.

Further research by Ge includes the development of predictive models for drug repurposing, such as a random forest model for SARS-CoV-2 main protease binding. He has also contributed to understanding genetic variation by assessing the reproducibility of inherited variants detected through whole-genome sequencing. His collaborators at the National Center for Toxicological Research include Joe Meehan, Bohu Pan, Baitang Ning, and Wen Zou, with whom he has co-authored numerous publications.

Ge's scholarship is recognized by a high-impact researcher designation, supported by 72 publications and over 6,700 citations, with an h-index of 25. His recent work demonstrates a continued engagement with advanced computational techniques for biological and chemical data analysis, including the application of deep learning to nanomaterial properties and the use of natural language processing for health-related data.

Metrics

  • h-index: 25
  • Publications: 72
  • Citations: 6,831

Selected Publications

  • Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques (2025)
    Experimental Biology and Medicine 4 citations DOI OpenAlex
  • AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women (2025)
    Experimental Biology and Medicine 49 citations DOI OpenAlex
  • Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study (2024)
    Expert Opinion on Drug Metabolism & Toxicology 27 citations DOI OpenAlex
  • A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database (2023)
    Experimental Biology and Medicine 7 citations DOI OpenAlex
  • Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment (2023)
    Experimental Biology and Medicine 12 citations DOI OpenAlex
  • Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals (2023)
    Computational methods in engineering & the sciences 3 citations DOI OpenAlex
  • Additional file 3 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022)
    Figshare DOI OpenAlex
  • Additional file 13 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022)
    Figshare DOI OpenAlex
  • Additional file 5 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022)
    Figshare DOI OpenAlex
  • Additional file 15 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022)
    Figshare DOI OpenAlex
  • Additional file 10 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022)
    Figshare DOI OpenAlex
  • Additional file 9 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022)
    Figshare DOI OpenAlex
  • Additional file 11 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022)
    Figshare DOI OpenAlex
  • Additional file 1 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022)
    Figshare DOI OpenAlex
  • Additional file 6 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022)
    Figshare DOI OpenAlex

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