James Lindsay Source Confirmed
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Associate Director: Knowledge Systems Group
University of Arkansas at Fayetteville
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Biography and Research Information
OverviewAI-generated summary
James Lindsay's research focuses on the analysis of genomic and clinical data, particularly within the context of cancer research and treatment. He has been involved in large-scale collaborative projects, such as the AACR Project GENIE Biopharma Collaborative, which aims to collect and analyze genomic and clinical data from a substantial number of cancer cases. His work investigates the correlations between genomic alterations, such as high tumor mutation burden and specific gene mutations like SMARCA4 and KRASG12D, and patient outcomes, especially in response to immune checkpoint inhibitors and other targeted therapies in non-small cell lung cancer (NSCLC) and metastatic colorectal cancer.
Lindsay's publications also explore the role of the tumor microenvironment and immune cell infiltration in predicting treatment response. He has examined the association of factors like neutrophil-to-lymphocyte ratio (dNLR) with increased T-cell infiltration and favorable outcomes in NSCLC patients receiving pembrolizumab. Furthermore, his work has extended to clinical trial designs, including randomized trials evaluating combined immunotherapy and radiation for metastatic colorectal cancer, and studies on the management of inflammatory conditions like Crohn's disease.
With a significant publication record (462 total publications) and a high citation count (4,621), Lindsay is recognized as a highly cited researcher. He holds the position of Associate Director for the Knowledge Systems Group at the University of Arkansas at Fayetteville. His collaborators include researchers from institutions such as Lyon College, indicating a network of academic partnerships in his research endeavors.
Metrics
- h-index: 22
- Publications: 462
- Citations: 4,621
Selected Publications
- Prediction of aflatoxin contamination outbreaks in Texas corn using mechanistic and machine learning models (2025) DOI
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