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1 Ergebnisse
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Hours taken for different GRN learning methods for Network ..:
Niko Bernaola
;
Mario Michiels
;
Pedro Larrañaga
.
doi:10.1371/journal.pcbi.1011443.g009. , 2023
Link:
https://doi.org/10.1371/journal.pcbi.1011443.g009
RT Journal T1
Hours taken for different GRN learning methods for Network 1 of the Network Inference Challenge
UL https://suche.suub.uni-bremen.de/peid=base-ftdeakinunifig:oai:figshare.com:article_24714553&Exemplar=1&LAN=DE A1 Niko Bernaola A1 Mario Michiels A1 Pedro Larrañaga A1 Concha Bielza YR 2023 K1 Genetics K1 Science Policy K1 Infectious Diseases K1 Environmental Sciences not elsewhere classified K1 Biological Sciences not elsewhere classified K1 Information Systems not elsewhere classified K1 use empirical knowledge K1 matthews correlation coefficient K1 gene regulatory network K1 fges )- merge K1 explainable artificial intelligence K1 bayesian network model K1 access visualization tool K1 gene regulatory networks K1 different brain structures K1 also improving upon K1 massive networks K1 human brain K1 xlink "> K1 predict interactions K1 new open K1 finding nodes K1 current trends K1 also present JF doi:10.1371/journal.pcbi.1011443.g009 LK http://dx.doi.org/https://doi.org/10.1371/journal.pcbi.1011443.g009 DO https://doi.org/10.1371/journal.pcbi.1011443.g009 SF ELIB - SuUB Bremen
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