Zobrazeno 1 - 10
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pro vyhledávání: '"Brian J. Parker"'
CRMnet: A deep learning model for predicting gene expression from large regulatory sequence datasets
Publikováno v:
Frontiers in Big Data, Vol 6 (2023)
Recent large datasets measuring the gene expression of millions of possible gene promoter sequences provide a resource to design and train optimized deep neural network architectures to predict expression from sequences. High predictive performance d
Externí odkaz:
https://doaj.org/article/cace88912d0c4c69a50d87c4e313777a
Publikováno v:
مجلة الدراسات الاجتماعية, Vol 4 (2014)
Abstruct:
Externí odkaz:
https://doaj.org/article/15d0bbc6ae3249b8a05be6de0a07cb24
Publikováno v:
مجلة الدراسات الاجتماعية, Vol 3 (2014)
Abstruct:
Externí odkaz:
https://doaj.org/article/c4c2542538ae400b93e929f608163af9
Autor:
Henry J. Sutton, Brian J. Parker, Mariah Lofgren, Cherrelle Dacon, Deepyan Chatterjee, Xin Gao, Hannah G. Kelly, Robert A. Seder, Joshua Tan, Azza H. Idris, Teresa Neeman, Ian A. Cockburn
SummaryLong-lived plasma cells (PCs) secrete antibodies that can provide sustained immunity against infection. It has been proposed that high affinity cells are preferentially selected into this compartment, potentiating the immune response. Here we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a523a98e77df49cfa3bf9033d96ff92
https://doi.org/10.1101/2023.04.13.536825
https://doi.org/10.1101/2023.04.13.536825
CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets
Recent large datasets measuring the gene expression of millions of possible gene promoter sequences provide a resource to design and train optimised deep neural network architectures to predict expression from sequences. High predictive performance d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c8db8d6427ff696acaf7d2b526b222c
https://doi.org/10.1101/2022.12.02.518786
https://doi.org/10.1101/2022.12.02.518786
Publikováno v:
Journal of Visualized Experiments.
As well as the typical analysis of RNA-Seq to measure differential gene expression (DGE) across experimental/biological conditions, RNA-seq data can also be utilized to explore other complex regulatory mechanisms at the exon level. Alternative splici
Publikováno v:
Clinical Medicine & Research. 15:37-40
Sternocostoclavicular hyperostosis (SCCH) is an infrequent chronic inflammatory disorder of the axial skeleton of unknown origin. SCCH goes often unrecognized due to a low level of awareness for the disorder. It typically presents with relapsing and
Autor:
Traude H. Beilharz, David T. Humphreys, Jennifer L. Clancy, Rina Soetanto, Brian J. Parker, Maurits Evers, Robert M. Graham, Stuart K. Archer, Guowen Duan, Thomas Preiss, Hardip R. Patel, Nicola J. Smith, Carly J. Hynes
Publikováno v:
Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms. 1859:744-756
miRNAs play critical roles in heart disease. In addition to differential miRNA expression, miRNA-mediated control is also affected by variable miRNA processing or alternative 3'-end cleavage and polyadenylation (APA) of their mRNA targets. To what ex
Autor:
Shuvadeep Maity, Yun Bin Matteo Zhang, Justin Rendleman, Scott Kuersten, Amy Lei, Zhe Cheng, Lindsay Freeberg, Markus Landthaler, Hyungwon Choi, Kristina Allgoewer, Brian J. Parker, Guoshou Teo, Christine Vogel, Mathias Munschauer, Nicolai Kastelic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7095cdd2d8eb4db7ebf7cf96727aba5c
https://doi.org/10.7554/elife.39054.047
https://doi.org/10.7554/elife.39054.047
Autor:
Brian J. Parker, Lindsay Freeberg, Kristina Allgoewer, Scott Kuersten, Justin Rendleman, Guoshou Teo, Amy Lei, Markus Landthaler, Yun Bin Matteo Zhang, Hyungwon Choi, Christine Vogel, Nicolai Kastelic, Mathias Munschauer, Shuvadeep Maity, Zhe Cheng
The mammalian response to endoplasmic reticulum (ER) stress dynamically affects all layers of gene expression regulation. We quantified transcript and protein abundance along with footprints of ribosomes and non-ribosomal proteins for thousands of ge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1993bbf886f74f5dd7e848a86692cc19
https://doi.org/10.1101/308379
https://doi.org/10.1101/308379