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Xingqiao Wang Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
faculty
9 h-index
44 pubs
334 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Xingqiao Wang's research focuses on the application of advanced computational methods, particularly large language models (LLMs) and transformer-based frameworks, to complex problems in health and medicine. A significant area of his work involves enhancing pharmacovigilance and patient safety through causal inference from free-text data, as demonstrated by projects like InferBERT and DeepCausality. These methods aim to identify potential drug-induced adverse events and understand disease mechanisms by analyzing large datasets.
Wang also investigates biological pathways and potential therapeutic targets. His publications include work on identifying genes like WDR43, DOK3, and PAPOLA as targets for conditions such as cerebral vasospasm, dementia, neuroinflammation, and tumorigenic processes. He has collaborated with researchers including Vivek Gunasekaran and Weida Tong on these and related topics. Wang's work leverages LLMs for tasks such as entity resolution and data linkage, contributing to the development of tools for efficient data analysis in biomedical research.
Metrics
- h-index: 9
- Publications: 44
- Citations: 334
Selected Publications
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OmniMatch: A Large Language Model-Based Data Linkage Tool (2024)
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Train Once, Match Everywhere: Harnessing Generative Language Models for Entity Matching (2023)
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Bidirectional Encoder Representations from Transformers-like large language models in patient safety and pharmacovigilance: A comprehensive assessment of causal inference implications (2023)
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DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox (2022)
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InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance (2021)
Collaboration Network
Top Collaborators
Xiaowei Xu
5 shared publications
- Bidirectional Encoder Representations from Transformers-like large language models in patient safety and pharmacovigilance: A comprehensive assessment of causal inference implications
- Train Once, Match Everywhere: Harnessing Generative Language Models for Entity Matching
- Leveraging large language models for efficient representation learning for entity resolution
- OmniMatch: A Large Language Model-Based Data Linkage Tool
- Leveraging large language models for efficient representation learning for entity resolution
Weida Tong
National Center for Toxicological Research (US)
3 shared publications
- InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance
- DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox
- Bidirectional Encoder Representations from Transformers-like large language models in patient safety and pharmacovigilance: A comprehensive assessment of causal inference implications
Bi T. Foua
3 shared publications
- Train Once, Match Everywhere: Harnessing Generative Language Models for Entity Matching
- Leveraging large language models for efficient representation learning for entity resolution
- Leveraging large language models for efficient representation learning for entity resolution
John R. Talburt
3 shared publications
- Train Once, Match Everywhere: Harnessing Generative Language Models for Entity Matching
- Leveraging large language models for efficient representation learning for entity resolution
- Leveraging large language models for efficient representation learning for entity resolution
Vivek Gunasekaran
3 shared publications
In database
- Leveraging large language models for efficient representation learning for entity resolution
- OmniMatch: A Large Language Model-Based Data Linkage Tool
- Leveraging large language models for efficient representation learning for entity resolution
Xiaowei Xu
University of Arkansas at Little Rock (US)
2 shared publications
- InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance
- DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox
Ruth Roberts
Alcon (United Kingdom) (GB)
1 shared publication
- InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance
Zhichao Liu
National Center for Toxicological Research (US)
1 shared publication
- InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance
Qi Liu
United States Food and Drug Administration (US)
1 shared publication
- DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox
Zhichao Liu
United States Food and Drug Administration (US)
1 shared publication
- DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox
Shuai Wang
Tianjin Polytechnic University (CN)
1 shared publication
- Research on trajectory tracking control algorithm in 4WS mode
Fanqiang Kong
Yuhuangding Hospital (CN)
1 shared publication
- Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43
Zengbin Lin
Qingdao University (CN)
1 shared publication
- Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43
Zhichao Liu
Boehringer Ingelheim (United States) (US)
1 shared publication
- Bidirectional Encoder Representations from Transformers-like large language models in patient safety and pharmacovigilance: A comprehensive assessment of causal inference implications
Jonathan White
United States Census Bureau (US)
1 shared publication
- OmniMatch: A Large Language Model-Based Data Linkage Tool
Similar Researchers
Based on overlapping research topics
Leihong Wu
National Center for Toxicological Research
Advanced Neural Network Applications
Natural Language Processing Techniques
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L. Lynn Harris
University of Arkansas at Fayetteville
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Dong Wang
National Center for Toxicological Research
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Disease Mechanisms Research
Se-Ran Jun
University of Arkansas for Medical Sciences
Advanced Neural Network Applications
Natural Language Processing Techniques
Bioinformatics and Genomic Networks
Ian Wayne Vance
University of Arkansas at Fayetteville
Advanced Neural Network Applications
Bioinformatics and Genomic Networks
Machine Learning Applications
Zachary D Zbinden
University of Arkansas at Fayetteville
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