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Title: Protocol to identify functional doppelgängers and verify biomedical gene expression data using doppelgangerIdentifier
Authors: Wang, Li Rong
Fan, Xiuyi
Goh, Wilson Wen Bin
Keywords: Engineering::Computer science and engineering
Science::Biological sciences
Issue Date: 2022
Source: Wang, L. R., Fan, X. & Goh, W. W. B. (2022). Protocol to identify functional doppelgängers and verify biomedical gene expression data using doppelgangerIdentifier. STAR Protocols, 3(4), 101783-.
Project: RG35/20
Journal: STAR Protocols
Abstract: Functional doppelgängers (FDs) are independently derived sample pairs that confound machine learning model (ML) performance when assorted across training and validation sets. Here, we detail the use of doppelgangerIdentifier (DI), providing software installation, data preparation, doppelgänger identification, and functional testing steps. We demonstrate examples with biomedical gene expression data. We also provide guidelines for the selection of user-defined function arguments. For complete details on the use and execution of this protocol, please refer to Wang et al. (2022).
ISSN: 2666-1667
DOI: 10.1016/j.xpro.2022.101783
Schools: School of Computer Science and Engineering 
Lee Kong Chian School of Medicine (LKCMedicine) 
School of Biological Sciences 
Research Centres: Centre for Biomedical Informatics
Rights: © 2022 The Author(s). This is an open access article under the CC BY-NC-ND license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles
SBS Journal Articles
SCSE Journal Articles

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