--- title: "BioTopicXplor — paper by Yeganova et al. (2026) · BioNLP 2026" description: "The authors present BioTopicXplor, a free web tool that takes a PubMed query and retrieves titles and abstracts, by default… 9 references." authors: ["Lana Yeganova", "Donald Comeau", "Won Kim", "Natalie Xie", "Shubo Tian", "W John Wilbur", "Zhiyong Lu"] year: 2026 venue: "BioNLP 2026" source: "https://aclanthology.org/2026.bionlp-1.37/" doi: "10.18653/v1/2026.bionlp-1.37" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/yeganova-2026-biotopicxplor-web-tool-interactive-exploration/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yeganova-2026-biotopicxplor-web-tool-interactive-exploration/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/25/) / BioTopicXplor: A Web Tool for Interactive Exploration of PubMed Literature through Reproducible Topics. # BioTopicXplor: A Web Tool for Interactive Exploration of PubMed Literature through Reproducible Topics. **Authors:** Lana Yeganova, Donald Comeau, Won Kim, Natalie Xie, Shubo Tian, W John Wilbur and [Zhiyong Lu](https://md.research.elamur.ai/free/en/authors/zhiyong-lu/) · **Year:** 2026 · **Venue:** [BioNLP 2026](https://md.research.elamur.ai/free/en/venues/bionlp/) ([BioNLP 2026](https://md.research.elamur.ai/free/en/venues/bionlp/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.bionlp-1.37/) · [PDF](https://aclanthology.org/2026.bionlp-1.37.pdf) · [DOI 10.18653/v1/2026.bionlp-1.37](https://doi.org/10.18653/v1/2026.bionlp-1.37) · [OpenAlex](https://openalex.org/W7166877683) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary PubMed finds articles for a query but does not show how a result set is structured conceptually. Companion tools such as Anne O'Tate, BioTextQuest v2.0 and GoPubMed organize literature through ontologies, user-chosen terms or features. Clustering methods like K-means can also give different topics on each run. The authors present BioTopicXplor, a free web tool that takes a PubMed query and retrieves titles and abstracts, by default from the past five years. It then discovers subtopics with ConvexTopics. This method treats clustering as a convex problem whose candidate centers are exemplars taken from the data. As a result, it reaches a global optimum, needs no preset topic count and returns the same subtopics on repeated runs. It operates on enriched single terms and noun phrases compared with the Dice coefficient. GPT-5.1 writes PMID-linked summaries and titles from each subtopic's ten top documents and groups subtopics into major topics, which users can browse or search. A case study on 12,000 anti-aging articles illustrates the workflow. The tool's value lies in pairing stable, data-driven topics with LLM output anchored to them, which limits hallucination. ## Key points - **Cluster handling:** Clusters that share more than 50% of their documents are removed. Each subtopic gets three scores: intrinsic quality, size, and visibility (how prominent its top-10 terms are in its top-10 documents). - **Evaluation:** - ConvexTopics was evaluated in a companion paper on 20-Newsgroups, Reuters-21578 and PubMed sets (Anti-Aging, Diabetes Mellitus, Age-Related Macular Degeneration). It compared favorably with K-means, LDA and BERTopic. - Major topics were checked only by manual inspection and may vary somewhat between runs. - **Runtime:** For the query "familial Mediterranean fever", 1,099 documents took 65 minutes and 5,731 documents took 87 minutes. LLM summarization and grouping, not clustering, account for most of the time. - **Implementation:** The front end uses HTML, CSS and JavaScript, and the back end uses C++ and Python on Linux. It runs on a shared 12-core Xeon Gold machine (up to 2.30 GHz) with 196 GB RAM. - **Anti-aging use case:** The analysis indicated that the safety evidence for widely sold NAD supplements is weak. Gaps include bioavailability, long-term safety studies and optimal dosing. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [National Institutes of Health](https://md.research.elamur.ai/free/en/government/national-institutes-of-health/) — Government body, Bethesda, United States · 13 works · 3,846 citations - [United States National Library of Medicine](https://md.research.elamur.ai/free/en/labs/united-states-national-library-of-medicine/) — Research institute, Bethesda, United States · 8 works · 5,342 citations 📚 Shelves (8): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,954 works · [🧬 Biochemistry, Genetics and Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/) · 263 works · [🧬 Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/molecular-biology/) · 260 works · [🤝 General Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/) · 254 works ## Cite Cite as: Lana Yeganova, Donald Comeau, Won Kim, Natalie Xie, Shubo Tian, W John Wilbur and Zhiyong Lu. 2026. BioTopicXplor: A Web Tool for Interactive Exploration of PubMed Literature through Reproducible Topics. BioNLP 2026. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{yeganova2026biotopicxplor, title = {{BioTopicXplor: A Web Tool for Interactive Exploration of PubMed Literature through Reproducible Topics.}}, author = {Lana Yeganova, Donald Comeau, Won Kim, Natalie Xie, Shubo Tian, W John Wilbur and Zhiyong Lu}, booktitle = {BioNLP 2026}, year = {2026}, doi = {10.18653/v1/2026.bionlp-1.37}, url = {https://doi.org/10.18653/v1/2026.bionlp-1.37} } ``` - [← previous BioNLP at #SMM4H-HeaRD 2026 Task 3 Estimating Flu Vaccine Effectiveness: A Temporal-Aware Fine-Tuning and Similarity-Based Few-Shot Prompting Approach](https://md.research.elamur.ai/free/en/papers/patularu-2026-bionlp-smm4h-heard-2026-task/) - [next → BioVLM: Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLMs](https://md.research.elamur.ai/free/en/papers/singha-2026-biovlm-routing-prompts-not-parameters/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/25/) ## References 9 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Keith D Engwall. 2017. Anne o’tate. Journal of the Medical Library Association: JMLA, 105(2):200. [find](https://scholar.google.com/scholar?q=Keith+D+Engwall%2E+2017%2E+Anne+o%E2%80%99tate%2E+Journal+of+the+Medical+Library+Association%3A+JMLA%2C+105%282%29%3A200%2E) 2. Stuart Lloyd. 1982. Least squares quantization in pcm. IEEE transactions on information theory, 28(2):129– 137. [find](https://scholar.google.com/scholar?q=Stuart+Lloyd%2E+1982%2E+Least+squares+quantization+in+pcm%2E+IEEE+transactions+on+information+theory%2C+28%282%29%3A129%E2%80%93+137%2E) 3. Qin Song, Xiaofeng Zhou, Kexin Xu, Sishi Liu, Xinqiang Zhu, and Jun Yang. 2023. The safety and antiaging effects of nicotinamide mononucleotide in human clinical trials: an update. Advances in nutrition, 14(6):1416–1435. [find](https://scholar.google.com/scholar?q=Qin+Song%2C+Xiaofeng+Zhou%2C+Kexin+Xu%2C+Sishi+Liu%2C+Xinqiang+Zhu%2C+and+Jun+Yang%2E+2023%2E+The+safety+and+antiaging+effects+of+nicotinamide+mononucleotide+in+human+clinical+trials%3A+an+update%2E+Advances+in+nutriti) 4. Theodosios Theodosiou, Konstantinos Vrettos, Ismini Baltsavia, Fotis Baltoumas, Nikolas Papanikolaou, Andreas N Antonakis, Dimitrios Mossialos, Christos A Ouzounis, Vasilis J Promponas, Makrina Karaglani, and 1 others. 2024. Biotextquest v2. 0: an evolved tool for biomedical literature mining and co… [find](https://scholar.google.com/scholar?q=Theodosios+Theodosiou%2C+Konstantinos+Vrettos%2C+Ismini+Baltsavia%2C+Fotis+Baltoumas%2C+Nikolas+Papanikolaou%2C+Andreas+N+Antonakis%2C+Dimitrios+Mossialos%2C+Christos+A+Ouzounis%2C+Vasilis+J+Promponas%2C+Makrina+Karagl) 5. Danial Lashkari and Polina Golland. 2007. Convex clustering with exemplar-based models. Advances in neural information processing systems, 20. [find](https://scholar.google.com/scholar?q=Danial+Lashkari+and+Polina+Golland%2E+2007%2E+Convex+clustering+with+exemplar%2Dbased+models%2E+Advances+in+neural+information+processing+systems%2C+20%2E) 6. Lana Yeganova, Donald C Comeau, Won Kim, and W John Wilbur. 2009. How to interpret pubmed queries and why it matters. Journal of the American Society for Information Science and Technology, 60(2):264–274. [find](https://scholar.google.com/scholar?q=Lana+Yeganova%2C+Donald+C+Comeau%2C+Won+Kim%2C+and+W+John+Wilbur%2E+2009%2E+How+to+interpret+pubmed+queries+and+why+it+matters%2E+Journal+of+the+American+Society+for+Information+Science+and+Technology%2C+60%282%29%3A264%E2%80%93) 7. David D. Lewis, Yiming Yang, Tony G. Rose, and Fan Li. 2004. [Rcv1: A new benchmark collection for text categorization research](https://md.research.elamur.ai/free/en/papers/lewis-2004-rcv1-benchmark-collection-text-categorization/). Journal of Machine Learning Research, 5:361–397. 8. Lana Yeganova, Donald C Comeau, and W John Wilbur. 2011. Machine learning with naturally labeled data for identifying abbreviation definitions. BMC bioinformatics, 12(Suppl 3):S6. [find](https://scholar.google.com/scholar?q=Lana+Yeganova%2C+Donald+C+Comeau%2C+and+W+John+Wilbur%2E+2011%2E+Machine+learning+with+naturally+labeled+data+for+identifying+abbreviation+definitions%2E+BMC+bioinformatics%2C+12%28Suppl+3%29%3AS6%2E) 9. Lana E. Yeganova, Won G. Kim, Shubo Tian, Natalie Xie, Donald C. Comeau, W. John Wilbur, and Zhiyong Lu. 2026. Exploring anti-aging literature via convextopics and large language models. AMIA Amplify Informatics Conference. [find](https://scholar.google.com/scholar?q=Lana+E%2E+Yeganova%2C+Won+G%2E+Kim%2C+Shubo+Tian%2C+Natalie+Xie%2C+Donald+C%2E+Comeau%2C+W%2E+John+Wilbur%2C+and+Zhiyong+Lu%2E+2026%2E+Exploring+anti%2Daging+literature+via+convextopics+and+large+language+models%2E+AMIA+Amplify+) *Updated 2 November 2026*