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Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep learning

Year: 2024

Type: article

Abstract: Supporting code for the manuscript "Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep learning", written by: Slavica Dimitrieva, Rens Janssens, Gang Li, Artur Szalata, Raja Gopal, Chintan Parmar, Audrey Kauffmann, Eric Y. Durand. MOBER (Multi Origin Batch Effect Remover) is a deep learning-based method that performs biologically relevant integration of transcriptional profiles from pre-clinical models and clinical tumors. MOBER can be used to guide the selection of cell lines and patient-derived xenografts and identify models that more closely resemble clinical tumors. Related repository: https://github.com/Novartis/MOBER

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14209838

Author: Dimitrieva, Slavica

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W6930552811

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