Robustness and reproducibility for AI learning in biomedical sciences: RENOIR.

ABSTRACT: Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large, complex datasets. Despite this trend, AI publications often suffer from lack of reproducibility and poor generalisation of findings, undermining scientific value and contributing to global research waste. To address these issues and focusing on the learning aspect of the AI field, we present RENOIR (REpeated random sampliNg fOr machIne leaRning), a modular open-source platform for robust and reproducible machine learning (ML) analysis. RENOIR adopts standardised pipelines for model training and testing, introducing elements of novelty, such as the dependence of the performance of the algorithm on the sample size. Additionally, RENOIR

SUBMITTER: Barberis A 

PROVIDER: S-EPMC10810363 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature