Building flexible and robust analysis frameworks for molecular subtyping of cancers
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| Název: | Building flexible and robust analysis frameworks for molecular subtyping of cancers |
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| Autoři: | 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 |
| Zdroj: | 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 |
| Informace o vydavateli: | Wiley, 2024. |
| Rok vydání: | 2024 |
| Témata: | 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 |
| Popis: | 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. |
| Druh dokumentu: | Article Other literature type |
| Popis souboru: | application/pdf |
| Jazyk: | English |
| ISSN: | 1878-0261 1574-7891 |
| DOI: | 10.1002/1878-0261.13580 |
| Přístupová URL adresa: | 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 |
| Přístupové číslo: | edsair.doi.dedup.....d78cf5d6920f03e8b6ec0dff7f1a0ade |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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. |
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| ISSN: | 18780261 15747891 |
| DOI: | 10.1002/1878-0261.13580 |