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Bettina Mieth

Showing results (1-10 of 4) with videos related to

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BMC Bioinformatics|June 17, 2011
FFCA: a feasibility-based method for flux coupling analysis of metabolic networksLaszlo David, Sayed-Amir Marashi, Abdelhalim Larhlimi, et al.
NAR Genomics and Bioinformatics|July 23, 2021
DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studiesBettina Mieth, Alexandre Rozier, Juan Antonio Rodriguez, et al.
Scientific Reports|January 1, 2020
Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq dataBettina Mieth, James R F Hockley, Nico Görnitz, et al.
Scientific Reports|November 29, 2016
Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association StudiesBettina Mieth, Marius Kloft, Juan Antonio Rodríguez, et al.
Pageof 1

Showing results (1-10 of 4) with videos related to

Sort By:
Pageof 1
BMC Bioinformatics|June 17, 2011
FFCA: a feasibility-based method for flux coupling analysis of metabolic networksLaszlo David, Sayed-Amir Marashi, Abdelhalim Larhlimi, et al.
NAR Genomics and Bioinformatics|July 23, 2021
DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studiesBettina Mieth, Alexandre Rozier, Juan Antonio Rodriguez, et al.
Scientific Reports|January 1, 2020
Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq dataBettina Mieth, James R F Hockley, Nico Görnitz, et al.
Scientific Reports|November 29, 2016
Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association StudiesBettina Mieth, Marius Kloft, Juan Antonio Rodríguez, et al.
Pageof 1