Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Daniel P. Gomari"'
Autor:
Daniel P. Gomari, Annalise Schweickart, Leandro Cerchietti, Elisabeth Paietta, Hugo Fernandez, Hassen Al-Amin, Karsten Suhre, Jan Krumsiek
Publikováno v:
Communications Biology, Vol 5, Iss 1, Pp 1-9 (2022)
Variable autoencoders offer an alternative way to interrogate metabolomic data and identify meaningful, non-linear relationships.
Externí odkaz:
https://doaj.org/article/3961c9d5fe67404480bd95c5c6999ff4
Autor:
Matthias Arnold, Daniel P. Gomari, Mustafa Buyukozkan, Kelsey Chetnik, Zeyu Wang, Jan Krumsiek, Jonas Zierer, Elisa Benedetti, Karsten Suhre, Richa Batra, Annalise Schweickart
Publikováno v:
Bioinformatics
This article presents maplet, an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for metabolomics data analysis. It builds on the SummarizedExperiment data structure to create a centralized pipe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::647f31206d207867e46585d9eccb4cfe
http://europepmc.org/articles/PMC8796365
http://europepmc.org/articles/PMC8796365
Autor:
Daniel P. Gomari, Annalise Schweickart, Leandro Cerchietti, Elisabeth Paietta, Hugo Fernandez, Hassen Al-Amin, Karsten Suhre, Jan Krumsiek
Publikováno v:
Comm. Biol. 5:645 (2022)
Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd49ec6ebdcc119f094d34eb225f04fe
https://pubmed.ncbi.nlm.nih.gov/35773471
https://pubmed.ncbi.nlm.nih.gov/35773471
Autor:
Annalise Schweickart, Hassen Al-Amin, Elisabeth Paietta, Daniel P. Gomari, Karsten Suhre, Hugo F. Fernandez, Jan Krumsiek, Leandro Cerchietti
Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f948c046d942907e6ad28acedff5f29b
https://doi.org/10.1101/2021.01.14.426721
https://doi.org/10.1101/2021.01.14.426721
