Protocol for obtaining cancer type and subtype predictions using subSCOPE

Protocol for obtaining cancer type and subtype predictions using subSCOPE

Summary: We present a protocol for obtaining cancer type and subtype predictions using a machine learning method (subSCOPE). We describe steps for data preparation, subSCOPE setup, and running subSCOPE inference on prepared data. The protocol supports five -omics data types as input (DNA methylation...

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Bibliographic Details
Main Authors: Jasleen K. Grewal, A. Gordon Robertson, Kyle Ellrott, Christopher K. Wong, Jordan A. Lee, Christina Yau, Bahar Tercan, Mauro A.A. Castro, Christopher C. Benz, Jean C. Zenklusen, Andrew D. Cherniack, Peter W. Laird, Steven J.M. Jones, Theo A. Knijnenburg, Vinicius S. Chagas, Victor H. Apolonio, Verena Friedl, Joshua M. Stuart, Vladislav Uzunangelov, Jesper B. Andersen, Galen F. Gao, Gad Getz, Stephanie H. Hoyt, Whijae Roh, Lindsay Westlake, Samantha J. Caesar-Johnson, John A. Demchok, Ina Felau, Anab Kemal, Roy Tarnuzzer, Zhining Wang, Liming Yang, Rehan Akbani, Bradley M. Broom, Zhenlin Ju, Andre Schultz, Akinyemi I. Ojesina, Katherine A. Hoadley, Avantika Lal, Daniele Ramazzotti, Chen Wang, Alexander J. Lazar, Lewis R. Roberts, Taek-Kyun Kim, Ilya Shmulevich, Paulos Charonyktakis, Vincenzo Lagani, Ioannis Tsamardinos, Esther Drill, Ronglai Shen, Martin L. Ferguson, Kami E. Chiotti, Brian J. Karlberg, Eve Lowenstein, Adam Struck, Paul T. Spellman, Toshinori Hinoue
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:STAR Protocols
Subjects:
Bioinformatics
Cancer
Genomics
Computer sciences
Online Access:http://www.sciencedirect.com/science/article/pii/S266616672500111X
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