Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer
2020 · Computational and Structural Biotechnology Journal · 36 citations
https://doi.org/10.1016/j.csbj.2020.05.021
D
David Chardin
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11
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David Chardin is an academic researcher from Centre Antoine Lacassagne. The author has contributed to research in topics: Cancer Immunotherapy and Biomarkers & Radiomics and Machine Learning in Medical Imaging & Medical Imaging Techniques and Applications. The author has an h-index of 10, co-authored 38 publications.
ORCID: 0000-0003-1497-1521Papers by this author
worksLearning a confidence score and the latent space of a new supervised autoencoder for diagnosis and prognosis in clinical metabolomic studies
2022 · BMC Bioinformatics · 13 citations
https://doi.org/10.1186/s12859-022-04900-x
[18F]FDG-PET/CT atypical response patterns to immunotherapy in non-small cell lung cancer patients: long term prognosis assessment and clinical management proposal
2024 · European Journal of Nuclear Medicine and Molecular Imaging · 9 citations
https://doi.org/10.1007/s00259-024-06794-8
Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data.
2023 · Computational and Structural Biotechnology Journal · 5 citations
https://doi.org/10.1016/j.csbj.2023.10.033
Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection—an application in metabolomics studies
2021 · HAL (Le Centre pour la Communication Scientifique Directe) · 0 citations
https://doi.org/10.6084/m9.figshare.c.5753470
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