A machine learning and directed network optimization approach to uncover TP53 regulatory patterns

Triantafyllidis, Charalampos P.; Barberis, Alessandro; Hartley, Fiona; Cuervo, Ana Miar; Gjerga, Enio; Charlton, Philip; van Bijsterveldt, Linda; Rodriguez, Julio Saez; Buffa, Francesca M.

doi:10.1016/j.isci.2023.108291

Cited by 1 publication

(2 citation statements)

References 57 publications

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“…To further emphasise the relevance of RENOIR to biomedical science, we highlight two recently published applications 24 , 25 .…”

Section: Resultsmentioning

confidence: 99%

“…Similar to the previously mentioned study, we fitted generalized linear models with L1/L2 penalisation using different training set sizes, with hyperparameters optimised in tenfold cross-validation. Notably, the identified best models demonstrated their efficacy in predicting TP53 mutation status (wild type/mutant) based on the expression of the TP53 regulon 25 .…”

Section: Resultsmentioning

confidence: 99%

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Robustness and reproducibility for AI learning in biomedical sciences: RENOIR

Barberis,

Aerts,

Buffa

2024

Sci Rep

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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 offers automated generation of transparent and usable reports, aiming to enhance the quality and reproducibility of AI studies. To demonstrate the versatility of our tool, we applied it to benchmark datasets from health, computer science, and STEM (Science, Technology, Engineering, and Mathematics) domains. Furthermore, we showcase RENOIR’s successful application in recently published studies, where it identified classifiers for SET2D and TP53 mutation status in cancer. Finally, we present a use case where RENOIR was employed to address a significant pharmacological challenge—predicting drug efficacy. RENOIR is freely available at https://github.com/alebarberis/renoir.

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“…To further emphasise the relevance of RENOIR to biomedical science, we highlight two recently published applications 24 , 25 .…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%