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Zhiyong Lu Publications - NIH
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<h3>Selected Publications (<a href="https://scholar.google.com/citations?hl=en&user=lJAkLo8AAAAJ&view_op=list_works"
target="_blank">click here to see all</a>)</h3>
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<h4>Medical Journals</h4>
<ul class="dot-list" style="font-size:16px;">
<li>
Wang et al.,
<a href='https://arxiv.org/abs/2405.16205'>GeneAgent: Self-verification language agent for gene set analysis using domain databases</a>
<em>Nature Methods</em>,
to appear.
</li>
<li>
Chen et al.,
<a href='https://arxiv.org/abs/2305.16326'>Benchmarking LLMs for biomedical NLP applications and recommendations</a>
<em>Nature Communications</em>,
In Press
</li>
<li>
Jin et al.,
<a href='https://www.nature.com/articles/s41467-024-53081-z'>Matching patients to clinical trials with large language models.</a>
<em>Nature Communications</em>,
2024
</li>
<li>
Allot et al.,
<a href='https://doi.org/10.1038/s41588-023-01414-x'>Tracking genetic variants in the biomedical literature using LitVar 2.0.</a>
<em>Nature Genetics</em>,
2023
</li>
<li>
Lin et al.,
<a href='https://www.nature.com/articles/s41467-023-41974-4'>Improving model fairness in image-based computer-aided diagnosis</a>
<em>Nature Communications</em>,
2023
</li>
<li>
Tram et al.,
<a href='https://www.nature.com/articles/s43587-022-00171-6'>Detecting visually significant cataract using retinal photograph-based deep learning.</a>
<em>Nature Aging</em>,
2022
</li>
<li>
Diaz-Pinto et al.,
<a href='https://www.nature.com/articles/s42256-021-00427-7'>Predicting myocardial infarction through retinal scans and minimal personal information</a>
<em>Nat Mach Intell</em>,
2022
</li>
<li>
Chen et al.,
<a href='https://pubmed.ncbi.nlm.nih.gov/32157233/'>LitCovid: Keep up with the latest coronavirus research</a>
<em>Nature</em>,
2020
</li>
<li>
Peng et al.,
<a href='https://www.nature.com/articles/s41746-020-00317-z'>Predicting risk of late age-related macular degeneration using deep learning</a>
<em>npj Digital Medicine</em>,
2020
</li>
<li>
Leaman et al.,
<a href='https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000716'>Ten tips for a text-mining-ready article: How to improve discoverability and interpretability</a>
<em>PLoS Biology</em>,
2020
</li>
<li>
Peng et al.,
<a href='https://linkinghub.elsevier.com/retrieve/pii/S0161-6420(18)32185-7'>DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs</a>
<em>Ophthalmology</em>,
2019
</li>
<li>
Fiorini et al.,
<a href='https://www.nature.com/articles/nbt.4267'>How user intelligence is improving PubMed.</a>
<em>Nature Biotechnology</em>,
2018
</li>
<li>
Fiorini et al.,
<a href='http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2005343'>Best Match: new relevance search for PubMed.</a>
<em>PLoS Biology</em>,
2018
</li>
<li>
Fiorini et al.,
<a href='https://pubmed.ncbi.nlm.nih.gov/29083299/'>Towards PubMed 2.0</a>
<em>Elife</em>,
2017
</li>
<li>
Wei et al.,
<a href='http://nar.oxfordjournals.org/content/41/W1/W518.long'>PubTator: a web-based text mining tool for assisting biocuration.</a>
<em>Nucleic Acids Res.</em>,
2013
</li>
</ul>
<h4>CS Conferences</h4>
<ul class="dot-list" style="font-size:16px;">
<li>
Khandekar et al.,
<a href='https://arxiv.org/abs/2406.12036'>Evaluating Large Language Models for Medical Calculations</a>
<em>NeurIPs (Oral)</em>,
2024
</li>
<li>
Xiong et al.,
<a href='https://aclanthology.org/2024.findings-acl.372.pdf'>Benchmarking Retrieval-Augmented Generation for Medicine</a>
<em>ACL (Findings)</em>,
2024
</li>
<li>
Jin et al.,
<a href='https://dl.acm.org/doi/abs/10.1145/3539618.3592005'>LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search</a>
<em>SIGIR</em>,
2023
</li>
<li>
Yan et al.,
<a href='https://openaccess.thecvf.com/content_CVPR_2019/html/Yan_Holistic_and_Comprehensive_Annotation_of_Clinically_Significant_Findings_on_Diverse_CVPR_2019_paper.html'>Holistic and comprehensive annotation of clinically significant findings on diverse CT images: learning from radiology reports and label ontology.</a>
<em>CVPR</em>,
2019
</li>
<li>
Mohan et al.,
<a href='https://arxiv.org/abs/1802.10078'>A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval</a>
<em>WWW</em>,
2018
</li>
<li>
Wang et al.,
<a href='https://openaccess.thecvf.com/content_cvpr_2018/html/Wang_TieNet_Text-Image_Embedding_CVPR_2018_paper.html'>Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays</a>
<em>CVPR</em>,
2018
</li>
<li>
Shen et al.,
<a href='https://www.kdd.org/kdd2018/files/project-showcase/KDD18_paper_1815.pdf'>SetSearch+: Entity-Set-Aware Search and Mining for Scientific Literature.</a>
<em>KDD</em>,
2018
</li>
<li>
Wang et al.,
<a href='http://www.cs.jhu.edu/%7Elelu/publication/CVPR2017_ChestX-Ray8.pdf'>ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases</a>
<em>CVPR</em>,
2017
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