Xingqiao Wang
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.
Researcher
Also affiliated: Qingdao University (2023–2024); Shenyang Ligong University (2015–2019); Jilin University (1989–2019); Tiangong University (2023); Chinese Academy of Sciences (2010); Yuhuangding Hospital (2023–2024); Changchun Institute of Applied Chemistry (2010); Jilin Medical University (2006); Jilin Agricultural University (2007); Changchun University (2007); East China Normal University (2012); Xi'an Jiaotong University (2019); Tsinghua University (2011)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Xingqiao Wang's research focuses on the application of advanced computational methods, particularly large language models and causal inference frameworks, to address complex challenges in health and medicine. His work investigates the use of artificial intelligence for enhancing pharmacovigilance and patient safety, as demonstrated by publications on frameworks like InferBERT and DeepCausality. These tools aim to extract meaningful insights from unstructured text data to improve drug safety monitoring and understand disease mechanisms. Wang also explores gene expression analysis to identify potential therapeutic targets for neuroinflammatory and tumorigenic processes, as seen in his recent cross-disease transcriptomic studies. His research network includes collaborators such as John R. Talburt and Vivek Gunasekaran from the University of Arkansas at Little Rock, and Weida Tong from the National Center for Toxicological Research, with whom he has co-authored multiple publications. Wang's scholarship metrics include an h-index of 9, with 44 total publications and 336 citations.
Metrics
- h-index: 9
- Publications: 45
- Citations: 340
Selected Publications
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ARISE: Agentic Rubric-Guided Iterative Survey Engine for Automated Scholarly Paper Generation (2026)
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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
- 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
- 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
- OmniMatch: A Large Language Model-Based Data Linkage Tool
- 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
- InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance
- 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
- 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
- Train Once, Match Everywhere: Harnessing Generative Language Models for Entity Matching
- Train Once, Match Everywhere: Harnessing Generative Language Models for Entity Matching
- OmniMatch: A Large Language Model-Based Data Linkage Tool
- OmniMatch: A Large Language Model-Based Data Linkage Tool
- OmniMatch: A Large Language Model-Based Data Linkage Tool
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