Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Apr 24;7(3):36.
doi: 10.3390/mps7030036.

Simplifying Data Analysis in Biomedical Research: An Automated, User-Friendly Tool

Rúben Araújo  1   2   3 , Luís Ramalhete  1   4   5 , Ana Viegas  2   6   7 , Cristiana P Von Rekowski  1   2   3 , Tiago A H Fonseca  1   2   3 , Cecília R C Calado  3   8 , Luís Bento  9   10
Affiliations

Simplifying Data Analysis in Biomedical Research: An Automated, User-Friendly Tool

Rúben Araújo et al. Methods Protoc. .

Abstract

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 disparities 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.

Keywords: LLM models; biomedical research; high dimensional data analysis; machine learning.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
User experience flowchart: dashed blue rectangle outlines the main functions of ArsHive, whereas the dashed green rectangle indicates the AI-assisted path.
Figure 2
Figure 2
Main GUI of ArsHive, displayed in its default (light) theme.
Figure 3
Figure 3
Roadmap for the development of ArsHive software version 0.90.
Figure 4
Figure 4
Comprehensive reporting in ArsHive. (A) Integrated report view within the GUI; (B) a snippet of the dedicated report window, providing detailed insights and comparison of data pre- and post-normalization.
Figure 4
Figure 4
Comprehensive reporting in ArsHive. (A) Integrated report view within the GUI; (B) a snippet of the dedicated report window, providing detailed insights and comparison of data pre- and post-normalization.
Figure 5
Figure 5
Interface overview and cost estimation with A.D.A. in ArsHive.
Figure 6
Figure 6
Conceptualization of future GUI.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Langley G.R., Adcock I.M., Busquet F., Crofton K.M., Csernok E., Giese C., Heinonen T., Herrmann K., Hofmann-Apitius M., Landesmann B., et al. Towards a 21st-century roadmap for biomedical research and drug discovery: Consensus report and recommendations. Drug Discov. Today. 2017;22:327–339. doi: 10.1016/j.drudis.2016.10.011. - DOI - PubMed
    1. Keramaris N.C., Kanakaris N.K., Tzioupis C., Kontakis G., Giannoudis P.V. Translational research: From benchside to bedside. Injury. 2008;39:643–650. doi: 10.1016/j.injury.2008.01.051. - DOI - PubMed
    1. Jarvis M.F., Williams M. Irreproducibility in Preclinical Biomedical Research: Perceptions, Uncertainties, and Knowledge Gaps. Trends Pharmacol. Sci. 2016;37:290–302. doi: 10.1016/j.tips.2015.12.001. - DOI - PubMed
    1. Frampton G., Whaley P., Bennett M., Bilotta G., Dorne J.-L.C.M., Eales J., James K., Kohl C., Land M., Livoreil B., et al. Principles and framework for assessing the risk of bias for studies included in comparative quantitative environmental systematic reviews. Environ. Evid. 2022;11:12. doi: 10.1186/s13750-022-00264-0. - DOI - PMC - PubMed
    1. Roberts C., Torgerson D.J. Understanding controlled trials: Baseline imbalance in randomised controlled trials. BMJ. 1999;319:185. doi: 10.1136/bmj.319.7203.185. - DOI - PMC - PubMed

LinkOut - more resources