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Collection : Mines Paristech

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Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology

Authors : Beaufrère, Aurélie; Lazard, Tristan; Nicolle, Rémy ...

Subjects: [SDV.MHEP.AHA]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO]; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]

Source: https://minesparis-psl.hal.science/hal-04887163 ; 2025.

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Democratizing computational pathology: optimized Whole Slide Image representations for The Cancer Genome Atlas

Authors : Lazard, Tristan; Lerousseau, Marvin; Gardrat, Sophie ...

Subjects: Computational Pathology; Self-supervised Learning; [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]

Source: https://minesparis-psl.hal.science/hal-04396311 ; 2024.

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Academic Journal

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Authors : Abécassis, Judith; Hamy, Anne-Sophie; Laurent, Cécile ...

Subjects: [SDV.CAN]Life Sciences [q-bio]/Cancer; [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry; Molecular Biology/Genomics [q-bio.GN]

Source: ISSN: 1932-6203.

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NetNorM: capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis

Authors : Le Morvan, Marine; Zinovyev, Andrei; Vert, Jean-Philippe ...

Subjects: [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]

Source: https://hal.science/hal-01341856 ; 2016.

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Dissertation/ Thesis

Learning from multiple genomic information in cancer for diagnosis and prognosis ; Apprentissage de données génomiques multiples pour le diagnostic et le pronostic du cancer

Authors : Moarii, Matahi;

Subjects: Supervised Analysis; Unsupervised Analysis; High-Dimensional Data

Source: https://pastel.hal.science/tel-01449202 ; Quantitative Methods [q-bio.QM]. Ecole Nationale Supérieure des Mines de Paris, 2015. English. ⟨NNT : 2015ENMP0086⟩.

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