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Orientador(es)
Resumo(s)
Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than dispari ties between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool’s functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI’s GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its
versatility for diverse research fields.
Descrição
Palavras-chave
biomedical research machine learning LLM models high dimensional data analysis
Contexto Educativo
Citação
Araújo Rúben, Ramalhete Luís, Viegas Ana, Von Rekowski Cristiana P, Fonseca Tiago AH, Calado Cecília RC, Bento Luís – Simplifying data analysis in biomedical research: an automated, user-friendly tool. Methods and Protocols. 2024, 7(3), 36; https://doi.org/10.3390/mps7030036
Editora
MDPI