AI-Based Nanotoxicity Data Extraction and Prediction of Nanotoxicity

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

With the growing use of nanomaterials (NMs), assessing their toxicity has become increasingly important. Among assessment methods, computational models for predicting nanotoxicity are emerging as alternatives to traditional in vitro and vivo assays, which involve high costs ethical concerns. As a result, qualitative quantitative importance data is now widely recognized. However, collecting large, high-quality both time-consuming labor-intensive. Artificial intelligence (AI)-based extraction techniques hold significant potential extracting organizing information from unstructured text. large language (LLMs) prompt engineering not been studied. In this study, we developed an AI-based automated pipeline facilitate efficient collection. The automation process was implemented using Python-based LangChain. We used 216 research articles training refine prompts evaluate LLM performance. Subsequently, most suitable with refined extract test data, 605 articles. performance on achieved F1D.E. (F1 score Data Extraction) ranging 84.6 % 87.6 across different LLMs. Furthermore, extracted dataset set, constructed machine learning (AutoML) that F1N.P. Nanotoxicity Prediction) exceeding 86.1 nanotoxicity. Additionally, assessed reliability applicability by comparing them terms ground truth, size, balance. This study highlights extraction, representing contribution research.

Language: Английский

Advanced Toxicity Assessment: A BiLSTM Approach for Nanoparticle Safety

2022 7th International Conference on Communication and Electronics Systems (ICCES), Journal Year: 2024, Volume and Issue: unknown, P. 351 - 357

Published: Dec. 16, 2024

Language: Английский