Sirtuin Inhibitor as a Novel Cell Cycle Checkpoint and Regulator of the TP53-MDM2 Pathway in Uveal Melanoma

Goldsmith, Zachary K.; McEwen, Matthew W.; Kibe, Mercy; Yuan, Kelley; Hua, Yang; Zhang, Qing; Chintalapudi, Sumana R; Gao, Bradley; Jablonsk, Monica M.; King, Benjamin; Lee, Ryan P.; Lepsch, Abigail; Patel, Neel; Wang, Xiang Di; Grossniklaus, Hans E.; Wilson, Matthew W.; Morales-Tirado, Vanessa M.

doi:10.17140/ooj-3-119

Cited by 2 publications

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study

Raja,

Munawar,

Mylonas

et al. 2024

JMIR Form Res

View full text Add to dashboard Cite

Background In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs). Objective The aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers. Methods We developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirectional Encoder Representations from Transformers (BERT) and its variants, such as distilBERT, SciBERT, PubmedBERT, and BioBERT. To evaluate the LLMs, we compiled a data set (retinal diseases [RenD] ) of 1000 ocular disease–related articles, which were expertly annotated by a panel of 6 specialists into 19 distinct categories. In addition to the classification of articles, we also performed analysis on different classified groups to find the patterns and trends in the field. Results The classification results demonstrate the effectiveness of LLMs in categorizing a large number of ophthalmology papers without human intervention. The model achieved a mean accuracy of 0.86 and a mean F1-score of 0.85 based on the RenD data set. Conclusions The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval. We performed a trend analysis that enables researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.

show abstract

Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study

Raja,

Munawar,

Mylonas

et al. 2024

JMIR Form Res

View full text Add to dashboard Cite

show abstract

Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study (Preprint)

Raja,

Munawar,

Mylonas

et al. 2023

Preprint

View full text Add to dashboard Cite

BACKGROUND - OBJECTIVE In this paper, we present an automated method for article classification, leveraging the power of Large Language Models (LLM). The primary focus is on the field of ophthalmology, but the model is extendable to other fields. METHODS We have developed a model based on Natural Language Processing (NLP) techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we have employed zero-shot learning (ZSL) LLM models and compared against Bidirectional and Auto-Regressive Transformers (BART) and its variants, and Bidirectional Encoder Representations from Transformers (BERT), and its variant such as distilBERT, SciBERT, PubmedBERT, BioBERT. RESULTS The classification results demonstrate the effectiveness of LLMs in categorizing the large number of ophthalmology papers without human intervention. To evaluate the LLMs, we compiled a dataset (RenD) of 1000 ocular disease-related articles, which were expertly annotated by a panel of six specialists into 15 distinct categories. The model achieved a mean accuracy of 0.86 and a mean F1 of 0.85 based on the RenD dataset. CONCLUSIONS The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval in other domains too. We performed trend analysis that enables the researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.

show abstract

Sirtuin Inhibitor as a Novel Cell Cycle Checkpoint and Regulator of the TP53-MDM2 Pathway in Uveal Melanoma

Cited by 2 publications

References 26 publications

Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study

Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study

Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study (Preprint)

Contact Info

Product

Resources

About