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Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows

MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a machine learning algorithm. We then provide optimized...

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Autores principales: Saini, Vijit, Joglekar, Mugdha V., Wong, Wilson K.M., Jiang, Guozhi, Nassif, Najah T., Simpson, Ann M., Ma, Ronald C.W., Dalgaard, Louise T., Hardikar, Anandwardhan A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8554629/
https://www.ncbi.nlm.nih.gov/pubmed/34746868
http://dx.doi.org/10.1016/j.xpro.2021.100910
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author Saini, Vijit
Joglekar, Mugdha V.
Wong, Wilson K.M.
Jiang, Guozhi
Nassif, Najah T.
Simpson, Ann M.
Ma, Ronald C.W.
Dalgaard, Louise T.
Hardikar, Anandwardhan A.
author_facet Saini, Vijit
Joglekar, Mugdha V.
Wong, Wilson K.M.
Jiang, Guozhi
Nassif, Najah T.
Simpson, Ann M.
Ma, Ronald C.W.
Dalgaard, Louise T.
Hardikar, Anandwardhan A.
author_sort Saini, Vijit
collection PubMed
description MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a machine learning algorithm. We then provide optimized techniques to validate the identified miRNAs through over-expression/loss-of-function studies. Overall, these protocols apply to any field in biology where high-dimensional data are produced. For complete details on the use and execution of this protocol, please refer to Wong et al. (2021a).
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spelling pubmed-85546292021-11-05 Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows Saini, Vijit Joglekar, Mugdha V. Wong, Wilson K.M. Jiang, Guozhi Nassif, Najah T. Simpson, Ann M. Ma, Ronald C.W. Dalgaard, Louise T. Hardikar, Anandwardhan A. STAR Protoc Protocol MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a machine learning algorithm. We then provide optimized techniques to validate the identified miRNAs through over-expression/loss-of-function studies. Overall, these protocols apply to any field in biology where high-dimensional data are produced. For complete details on the use and execution of this protocol, please refer to Wong et al. (2021a). Elsevier 2021-10-23 /pmc/articles/PMC8554629/ /pubmed/34746868 http://dx.doi.org/10.1016/j.xpro.2021.100910 Text en © 2021. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Saini, Vijit
Joglekar, Mugdha V.
Wong, Wilson K.M.
Jiang, Guozhi
Nassif, Najah T.
Simpson, Ann M.
Ma, Ronald C.W.
Dalgaard, Louise T.
Hardikar, Anandwardhan A.
Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows
title Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows
title_full Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows
title_fullStr Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows
title_full_unstemmed Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows
title_short Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows
title_sort manipulating cellular micrornas and analyzing high-dimensional gene expression data using machine learning workflows
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8554629/
https://www.ncbi.nlm.nih.gov/pubmed/34746868
http://dx.doi.org/10.1016/j.xpro.2021.100910
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