Modeling new trends in bone regeneration, using the BERTopic approach

Papers of special note have been highlighted as: • of interest; •• of considerable interest

References

• 1. Colangelo MT, Galli C, Guizzardi S. The effects of polydeoxyribonucleotide on wound healing and tissue regeneration: a systematic review of the literature. Regen. Med. doi: 10.2217/rme-2019-0118 (2020) (Online ahead of print).
  Google Scholar
• 2. Sharma P, Kumar P, Sharma R, Bhatt VD, Dhot PS. Tissue Engineering; Current Status & Futuristic Scope. J. Med. Life 12(3), 225–229 (2019).
  MedlineGoogle Scholar
• 3. Pedrero SG, Llamas-Sillero P, Serrano-López J. A Multidisciplinary Journey towards Bone Tissue Engineering. Materials 14(17), 4896 (2021).
  Medline CASGoogle Scholar
• 4. Zhou J, Zhang Z, Joseph J et al. Biomaterials and nanomedicine for bone regeneration: progress and future prospects. Exploration 1(2), 20210011 (2021).
  Google Scholar
• 5. Ashammakhi N, GhavamiNejad A, Tutar R et al. Highlights on Advancing Frontiers in Tissue Engineering. Tissue Eng. Part B. Rev. 28(3), 633–664 (2022).
  MedlineGoogle Scholar
• 6. Landhuis E. Scientific literature: information overload. Nature 535(7612), 457–458(2016).
  MedlineGoogle Scholar
• 7. Grivell L. Mining the bibliome: searching for a needle in a haystack? EMBO Rep. 3(3), 200–203 (2002).
  Medline CASGoogle Scholar
• 8. Hyland K. English for academic purposes and discourse analysis. In: The Routledge Handbook of Discourse Analysis. Taylor and Francis, London, UK, 412–423 (2013).
  Google Scholar
• 9. Vayansky I, Kumar SAP. A review of topic modeling methods. Inf. Syst. 94, 101582 (2020). • A well-crafted review on the available approaches for topic modeling in the pre-BERTopic era.
  Google Scholar
• 10. Tata S, Patel JM. Estimating the selectivity of tf-idf based cosine similarity predicates. ACM SIGMOD Record 36(2), 7–12 (2007).
  Google Scholar
• 11. Li Y, Yang T. Word Embedding for Understanding Natural Language: A Survey. In: Guide to Big Data Applications. Studies in Big Data, vol 26. Srinivasan S (Ed.). Springer, Cham (2018).
  Google Scholar
• 12. Liu Q, Kusner MJ, Blunsom P. A survey on contextual embeddings. ArXiv preprint arXiv:2003.07278 (2020).
  Google Scholar
• 13. Churchill R, Singh L. The evolution of topic modeling. ACM Computing Surveys 54(10s), 1–35 (2022).
  Google Scholar
• 14. Kherwa P, Bansal P. Topic Modeling: A Comprehensive Review. ICST Transactions on Scalable Information Systems 7(24), 159623 (2018).
  Google Scholar
• 15. Huang X, Liu X, Shang Y, Qiao F, Chen G. Current Trends in Research on Bone Regeneration: A Bibliometric Analysis. Biomed. Res. Int. 2020, 8787394 (2020).
  MedlineGoogle Scholar
• 16. Lin H, Wang X, Huang M et al. Research hotspots and trends of bone defects based on Web of Science: a bibliometric analysis. J. Orthop. Surg. Res. 15(1), 463 (2020). • This article is a state-of-the-art report on the trends in bone regeneration performed with a manual approach.
  MedlineGoogle Scholar
• 17. Chauhan U, Shah A. Topic Modeling Using Latent Dirichlet allocation. ACM Comput. Surv. 54(7), 1–35 (2022).
  Google Scholar
• 18. Angelov D. Top2vec: distributed representations of topics. ArXiv preprint arXiv:2008.09470 (2020).
  Google Scholar
• 19. Ayyadevara VK, Ayyadevara VK. Word2vec. Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R. 167–178 (2018).
  Google Scholar
• 20. Grootendorst M. BERTopic: neural topic modeling with a class-based TF-IDF procedure. ArXiv preprint arXiv:2203.05794 (2022). •• This is Grootendorst's original publication on the release of BERTopic.
  Google Scholar
• 21. Wolf T, Debut L, Sanh V et al. Transformers: State-of-the-Art Natural Language Processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, PA, USA, 38–45 (2020).
  Google Scholar
• 22. Bouraoui Z, Camacho-Collados J, Schockaert S. Inducing relational knowledge from BERT. Proceedings of the AAAI Conference on Artificial Intelligence. 34(05), 7456–7463 doi: 10.1609/aaai.v34i05.6242 (2020).
  Google Scholar
• 23. Randles BM, Pasquetto IV, Golshan MS, Borgman CL. Using the Jupyter notebook as a tool for open science: an empirical study. In: 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL). IEEE (2017).1–2
  Google Scholar
• 24. Petrelli M. Setting up your python environment, easily. In: Introduction to Python in Earth Science Data Analysis. Springer Textbooks in Earth Sciences, Geography and Environment. Springer, Cham (2021).
  Google Scholar
• 25. Chapman B, Chang J. Biopython: python tools for computational biology. ACM Sigbio Newsletter. 20(2), 15–19 (2000).
  Google Scholar
• 26. Mckinney W. Data Structures for Statistical Computing in Python. In: Proceedings of the 9th Python in Science Conference. van der Walt SMillman J (Eds). Austin, TX, USA, 51–56 (2010).
  Google Scholar
• 27. Sarica S, Luo J. Stopwords in technical language processing. PLoS ONE 16(8), e0254937 (2021).
  Medline CASGoogle Scholar
• 28. Si Y, Wang J, Xu H, Roberts K. Enhancing clinical concept extraction with contextual embeddings. J. Am. Med. Inform. Assoc. 26(11), 1297–1304 (2019).
  MedlineGoogle Scholar
• 29. Kukushkin K, Ryabov Y, Borovkov A. Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling. Data (Basel) 7(12), 173 (2022).
  Google Scholar
• 30. Bartal A, Jagodnik KM, Chan SJ, Babu MS, Dekel S. Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives. Am. J. Obstet Gynecol. MFM 5(3), 100834 (2023).
  MedlineGoogle Scholar
• 31. Campello RJGB, Kröger P, Sander J, Zimek A. Density-based clustering. Wiley Interdiscip Rev. Data Min. Knowl Discov. 10(2), e1343 (2020).
  Google Scholar
• 32. Viegas F, Canuto S, Gomes C et al. CluWords. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM, NYUSA, 753–761 (2019).
  Google Scholar
• 33. Pedregosa F, Varoquaux G, Gramfort A et al. Scikit-learn: machine learning in Python. 2011 (2019). Available online: scikit-learn.org (Accessed on 20 December 2021).
  Google Scholar
• 34. Cees Roele. Disinformation. Github (2022). https://github.com/ceesroele/disinformation/blob/main/DisinformationTopicsOverTime.ipynb
  Google Scholar
• 35. Virtanen P, Gommers R, Oliphant TE et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17(3), 261–272 (2020).
  Medline CASGoogle Scholar
• 36. Hunter JD. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 9(3), 90–95 (2007).
  Google Scholar
• 37. Waskom M. seaborn: statistical data visualization. J. Open Source Softw. 6(60), 3021 (2021).
  Google Scholar
• 38. Basmatkar P, Maurya M. An Overview of Contextual Topic Modeling Using Bidirectional Encoder Representations from Transformers [Internet]. 489–504 (2022). Available from: https://link.springer.com/10.1007/978-981-16-8862-1_32
  Google Scholar
• 39. Zeni M, Zhang W, Bignotti E, Passerini A, Giunchiglia F. Fixing Mislabeling by Human Annotators Leveraging Conflict Resolution and Prior Knowledge. Proc. ACM Interact Mob. Wearable Ubiquitous Technol. 3(1), 1–23 (2019).
  MedlineGoogle Scholar
• 40. Lloyd S. Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982).
  Google Scholar
• 41. Yang L-C, Liu F-H, Liu C-M, Yu C-H, Chang Y-C. Bibliometric analysis of top-cited articles in Journal of Dental Sciences. J. Dent. Sci. 18(1), 338–344 (2023). • Similarly, this is a nice review of the research trends in dental sciences, which largely overlap the topics of bone regeneration.
  MedlineGoogle Scholar
• 42. Corrado F, Marconcini S, Cosola S, Giammarinaro E, Covani U. Immediate Implant and Customized Healing Abutment Promotes Tissues Regeneration: A 5-Year Clinical Report. J. Oral. Implantol. 49(1), 19–24 (2023).
  MedlineGoogle Scholar
• 43. Yang S, Wang L, Feng S, Yang Q, Yu B, Tu M. Enhanced bone formation by strontium modified calcium sulfate hemihydrate in ovariectomized rat critical-size calvarial defects. Biomedical Materials 12(3), 035004 (2017).
  Google Scholar
• 44. Turney PD, Pantel P. From Frequency to Meaning: Vector Space Models of Semantics. Journal of Artificial Intelligence Research 37, 141–188 (2010).
  Google Scholar
• 45. Li B, Han L. Distance Weighted Cosine Similarity Measure for Text Classification. 611–618 (2013).
  Google Scholar
• 46. Nematzadeh A, Meylan SC, Griffiths TL. Evaluating Vector-Space Models of Word Representation, or, The Unreasonable Effectiveness of Counting Words Near Other Words. In: Proceedings of the 39th Annual Meeting of the Cognitive Science Society (CogSci 2017). Computational Foundations of Cognition, London, UK (2017).
  Google Scholar
• 47. Worth PJ. Word Embeddings and Semantic Spaces in Natural Language Processing. Int. J. Intell. Sci. 13(1), 1–21 (2023).
  Google Scholar
• 48. Amendola RL, Goetz DD, Liu SS, Callaghan JJ. Two- to 4-Year Followup of a Short Stem THA Construct: Excellent Fixation, Thigh Pain a Concern. Clin. Orthop. Relat. Res. 475(2), 375–383 (2017).
  MedlineGoogle Scholar
• 49. Tang G, Liu Z, Liu Y et al. Recent trends in the development of bone regenerative biomaterials. Front Cell Dev. Biol. 9, 665813 (2021).
  MedlineGoogle Scholar
• 50. Ma Z, Wang Y, Li H. Applications of extracellular vesicles in tissue regeneration. Biomicrofluidics 14(1), 011501 (2020). •• This is a nice review on a hot topic in cell biology, i.e. exosomes, which are extracellular vesicles that are being credited with a whole range of new biological functions.
  Medline CASGoogle Scholar
• 51. Alhosani HMMG, Ahamat A, Ismail N. Industrial Revolution 4.0 (IR 4.0) Competencies: A Literature Review Of Manufacturing Industry. Pt. 2 J. Legal Ethical & Regul. Isses 24(1), (2021).
  Google Scholar