Uncovering cell cycle speed modulations with statistical inference: Computational biology

Pengzhi Zhang; Guangyu Wang

doi:10.1038/s41592-024-02484-3

Uncovering cell cycle speed modulations with statistical inference: Computational biology

Pengzhi Zhang, Guangyu Wang

Research output: Contribution to journal › Comment/debate › peer-review

1 Scopus citations

Abstract

A Bayesian inference framework improves RNA velocity estimates to make them more coherent and biologically interpretable.

Original language	English (US)
Article number	39
Pages (from-to)	2231-2232
Number of pages	2
Journal	Nature Methods
Volume	21
Issue number	12
DOIs	https://doi.org/10.1038/s41592-024-02484-3
State	Accepted/In press - Oct 2024

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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10.1038/s41592-024-02484-3

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title = "Uncovering cell cycle speed modulations with statistical inference: Computational biology",

abstract = "A Bayesian inference framework improves RNA velocity estimates to make them more coherent and biologically interpretable.",

author = "Pengzhi Zhang and Guangyu Wang",

note = "Publisher Copyright: {\textcopyright} Springer Nature America, Inc. 2024.",

year = "2024",

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AU - Wang, Guangyu

N1 - Publisher Copyright: © Springer Nature America, Inc. 2024.

PY - 2024/10

Y1 - 2024/10

N2 - A Bayesian inference framework improves RNA velocity estimates to make them more coherent and biologically interpretable.

AB - A Bayesian inference framework improves RNA velocity estimates to make them more coherent and biologically interpretable.

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Uncovering cell cycle speed modulations with statistical inference: Computational biology

Abstract

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