Protocol to identify functional doppelgängers and verify biomedical gene expression data using doppelgangerIdentifier

Li Rong Wang; Xiuyi Fan; Wilson Wen Bin Goh

doi:10.1016/j.xpro.2022.101783

Protocol to identify functional doppelgängers and verify biomedical gene expression data using doppelgangerIdentifier

Li Rong Wang^*, Xiuyi Fan, Wilson Wen Bin Goh^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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).

Original language	English
Article number	101783
Journal	STAR Protocols
Volume	3
Issue number	4
DOIs	https://doi.org/10.1016/j.xpro.2022.101783
Publication status	Published - Dec 16 2022
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2022 The Author(s)

ASJC Scopus Subject Areas

General Neuroscience
General Biochemistry,Genetics and Molecular Biology
General Immunology and Microbiology

Keywords

Bioinformatics
Computer sciences

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10.1016/j.xpro.2022.101783

Cite this

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title = "Protocol to identify functional doppelg{\"a}ngers and verify biomedical gene expression data using doppelgangerIdentifier",

abstract = "Functional doppelg{\"a}ngers (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, doppelg{\"a}nger 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).",

keywords = "Bioinformatics, Computer sciences",

author = "Wang, {Li Rong} and Xiuyi Fan and Goh, {Wilson Wen Bin}",

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year = "2022",

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doi = "10.1016/j.xpro.2022.101783",

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AU - Wang, Li Rong

AU - Fan, Xiuyi

AU - Goh, Wilson Wen Bin

PY - 2022/12/16

Y1 - 2022/12/16

N2 - 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).

AB - 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).

KW - Bioinformatics

KW - Computer sciences

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JO - STAR Protocols

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ER -

Protocol to identify functional doppelgängers and verify biomedical gene expression data using doppelgangerIdentifier

Abstract

Bibliographical note

ASJC Scopus Subject Areas

Keywords

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