AI-based nanotoxicity data extraction and prediction of nanotoxicity
With the growing use of nanomaterials (NMs), assessing their toxicity has become increasingly important. Among toxicity assessment methods, computational models for predicting nanotoxicity are emerging as alternatives to traditional in vitro and in vivo assays, which involve high costs and ethical c...
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025001175 |
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| author | Eunyong Ha Seung Min Ha Zayakhuu Gerelkhuu Hyun-Yi Kim Tae Hyun Yoon |
| author_facet | Eunyong Ha Seung Min Ha Zayakhuu Gerelkhuu Hyun-Yi Kim Tae Hyun Yoon |
| author_sort | Eunyong Ha |
| collection | DOAJ |
| description | With the growing use of nanomaterials (NMs), assessing their toxicity has become increasingly important. Among toxicity assessment methods, computational models for predicting nanotoxicity are emerging as alternatives to traditional in vitro and in vivo assays, which involve high costs and ethical concerns. As a result, the qualitative and quantitative importance of data is now widely recognized. However, collecting large, high-quality data is both time-consuming and labor-intensive. Artificial intelligence (AI)-based data extraction techniques hold significant potential for extracting and organizing information from unstructured text. However, the use of large language models (LLMs) and prompt engineering for nanotoxicity data extraction has not been widely studied. In this study, we developed an AI-based automated data extraction pipeline to facilitate efficient data collection. The automation process was implemented using Python-based LangChain. We used 216 nanotoxicity research articles as training data to refine prompts and evaluate LLM performance. Subsequently, the most suitable LLM with refined prompts was used to extract test data, from 605 research articles. As a result, data extraction performance on training data achieved F1D.E. (F1 score for Data Extraction) ranging from 84.6 % to 87.6 % across different LLMs. Furthermore, using the extracted dataset from test set, we constructed automated machine learning (AutoML) models that achieved F1N.P. (F1 score for Nanotoxicity Prediction) exceeding 86.1 % in predicting nanotoxicity. Additionally, we assessed the reliability and applicability of models by comparing them in terms of ground truth, size, and balance. This study highlights the potential of AI-based data extraction, representing a significant contribution to nanotoxicity research. |
| format | Article |
| id | doaj-art-9cade041207a4cc786faa24f8c0bcf57 |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-9cade041207a4cc786faa24f8c0bcf572025-08-20T03:08:20ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-012913814810.1016/j.csbj.2025.03.052AI-based nanotoxicity data extraction and prediction of nanotoxicityEunyong Ha0Seung Min Ha1Zayakhuu Gerelkhuu2Hyun-Yi Kim3Tae Hyun Yoon4Department of Chemistry, Hanyang University, Seoul 04763, Republic of KoreaDepartment of Chemistry, Hanyang University, Seoul 04763, Republic of KoreaResearch Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea; Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of KoreaNGeneS Inc., Ansan-si 15495, Republic of KoreaDepartment of Chemistry, Hanyang University, Seoul 04763, Republic of Korea; Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea; Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of Korea; Yoon Idea Lab. Co. Ltd., Seoul 04763, Republic of Korea; Corresponding author at: Department of Chemistry, Hanyang University, Seoul 04763, Republic of Korea.With the growing use of nanomaterials (NMs), assessing their toxicity has become increasingly important. Among toxicity assessment methods, computational models for predicting nanotoxicity are emerging as alternatives to traditional in vitro and in vivo assays, which involve high costs and ethical concerns. As a result, the qualitative and quantitative importance of data is now widely recognized. However, collecting large, high-quality data is both time-consuming and labor-intensive. Artificial intelligence (AI)-based data extraction techniques hold significant potential for extracting and organizing information from unstructured text. However, the use of large language models (LLMs) and prompt engineering for nanotoxicity data extraction has not been widely studied. In this study, we developed an AI-based automated data extraction pipeline to facilitate efficient data collection. The automation process was implemented using Python-based LangChain. We used 216 nanotoxicity research articles as training data to refine prompts and evaluate LLM performance. Subsequently, the most suitable LLM with refined prompts was used to extract test data, from 605 research articles. As a result, data extraction performance on training data achieved F1D.E. (F1 score for Data Extraction) ranging from 84.6 % to 87.6 % across different LLMs. Furthermore, using the extracted dataset from test set, we constructed automated machine learning (AutoML) models that achieved F1N.P. (F1 score for Nanotoxicity Prediction) exceeding 86.1 % in predicting nanotoxicity. Additionally, we assessed the reliability and applicability of models by comparing them in terms of ground truth, size, and balance. This study highlights the potential of AI-based data extraction, representing a significant contribution to nanotoxicity research.http://www.sciencedirect.com/science/article/pii/S2001037025001175NanotoxicityLarge Language ModelsData extractionPrompt engineeringLangChainAutomated machine learning |
| spellingShingle | Eunyong Ha Seung Min Ha Zayakhuu Gerelkhuu Hyun-Yi Kim Tae Hyun Yoon AI-based nanotoxicity data extraction and prediction of nanotoxicity Computational and Structural Biotechnology Journal Nanotoxicity Large Language Models Data extraction Prompt engineering LangChain Automated machine learning |
| title | AI-based nanotoxicity data extraction and prediction of nanotoxicity |
| title_full | AI-based nanotoxicity data extraction and prediction of nanotoxicity |
| title_fullStr | AI-based nanotoxicity data extraction and prediction of nanotoxicity |
| title_full_unstemmed | AI-based nanotoxicity data extraction and prediction of nanotoxicity |
| title_short | AI-based nanotoxicity data extraction and prediction of nanotoxicity |
| title_sort | ai based nanotoxicity data extraction and prediction of nanotoxicity |
| topic | Nanotoxicity Large Language Models Data extraction Prompt engineering LangChain Automated machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025001175 |
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