A role for SETD2 loss in tumorigenesis through DNA methylation dysregulation

Javaid, H; Barberis, Alessandro; Chervova, Olga; Nassiri, Isar; Voloshin, Vitaly; Sato, Yusuke; Ogawa, Seishi; Fairfax, Benjamin P.; Buffa, Francesca M.; Humphrey, Timothy C.

doi:10.1186/s12885-023-11162-0

Cited by 4 publications

(3 citation statements)

References 99 publications

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“…RENOIR’s contribution enabled the identification of a 3-CpG model with an 89% accuracy in correctly classifying renal cancer samples as SETD2 cases or wild type (WT). Notably, the identified model was further validated in an external cohort of renal cancer patients, demonstrating a classification accuracy of 87% 24 .…”

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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Section: Resultsmentioning

confidence: 99%

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

Section: Resultsmentioning

confidence: 99%

Robustness and reproducibility for AI learning in biomedical sciences: RENOIR

Barberis,

Aerts,

Buffa

2024

Sci Rep

Self Cite

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“…To develop and validate a 3-CpG methylation signature to predict SETD2 mutation status Javaid et al, 2023 [38].…”

Section: Binomial Logistic Regressionmentioning

confidence: 99%

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis: A Systematic Review

Ozaki,

Broughton,

Abdollahi

et al. 2024

Preprint

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Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

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