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Building flexible and robust analysis frameworks for molecular subtyping of cancers

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Titel: Building flexible and robust analysis frameworks for molecular subtyping of cancers
Autoren: Christina Bligaard Pedersen, Benito Campos, Lasse Rene, Helene Scheel Wegener, Neeraja M. Krishnan, Binay Panda, Kristoffer Vitting‐Seerup, Maria Rossing, Frederik Otzen Bagger, Lars Rønn Olsen
Quelle: Mol Oncol
Molecular Oncology, Vol 18, Iss 3, Pp 606-619 (2024)
Pedersen, C B, Campos, B, Rene, L, Wegener, H S, Krishnan, N M, Panda, B, Vitting-Seerup, K, Rossing, M, Bagger, F O & Olsen, L R 2024, ' Building flexible and robust analysis frameworks for molecular subtyping of cancers ', Molecular Oncology, vol. 18, no. 3, pp. 606-619 . https://doi.org/10.1002/1878-0261.13580
Verlagsinformationen: Wiley, 2024.
Publikationsjahr: 2024
Schlagwörter: 0301 basic medicine, 0303 health sciences, Gene Expression Profiling, Sample classification, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Computational Biology, sample classification, Breast Neoplasms, bioinformatics workflows, Clinical bioinformatics, 3. Good health, Gene Expression Regulation, Neoplastic, molecular subtyping, 03 medical and health sciences, Bioinformatics workflows, Humans, RNA, Female, name=SDG 3 - Good Health and Well-being, Molecular subtyping, clinical bioinformatics, RC254-282, Research Articles
Beschreibung: Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in‐depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer.
Publikationsart: Article
Other literature type
Dateibeschreibung: application/pdf
Sprache: English
ISSN: 1878-0261
1574-7891
DOI: 10.1002/1878-0261.13580
Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38158740
https://doaj.org/article/91af8075ca0347618bf363e33adca13d
https://orbit.dtu.dk/en/publications/26c5857a-2c8c-46d1-aec5-bd093285566b
Rights: CC BY
Dokumentencode: edsair.doi.dedup.....d78cf5d6920f03e8b6ec0dff7f1a0ade
Datenbank: OpenAIRE
Beschreibung
Abstract:Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in‐depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer.
ISSN:18780261
15747891
DOI:10.1002/1878-0261.13580