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Scripts used to train and assess machine learning models as..:

Vadim Demichev (5560499) ; Pinkus Tober-Lau (11052729) ; Tatiana Nazarenko (11306199)...
https://figshare.com/articles/dataset/Scripts_used_to_train_and_assess_machine_learning_models_as_well_as_the_respective_input_proteomics_data_/18644350. , 2022

Link: https://doi.org/10.1371/journal.pdig.0000007.s005

RT Journal
T1 <Title>Scripts used to train and assess machine learning models as well as the respective input proteomics data</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_18644350&Exemplar=1&LAN=DE
				
A1 Vadim Demichev (5560499)
A1 Pinkus Tober-Lau (11052729)
A1 Tatiana Nazarenko (11306199)
A1 Oliver Lemke (5129699)
A1 Simran Kaur Aulakh (9352442)
A1 Harry J. Whitwell (11584336)
A1 Annika Röhl (11306202)
A1 Anja Freiwald (271151)
A1 Mirja Mittermaier (11306232)
A1 Lukasz Szyrwiel (11306205)
A1 Daniela Ludwig (9187151)
A1 Clara Correia-Melo (3070935)
A1 Lena J. Lippert (11965054)
A1 Elisa T. Helbig (11965057)
A1 Paula Stubbemann (11306211)
A1 Nadine Olk (11965060)
A1 Charlotte Thibeault (9187157)
A1 Nana-Maria Grüning (8266131)
A1 Oleg Blyuss (737754)
A1 Spyros Vernardis (11306217)
A1 Matthew White (290523)
A1 Christoph B. Messner (1295217)
A1 Michael Joannidis (15668)
A1 Thomas Sonnweber (5703710)
A1 Sebastian J. Klein (10693815)
A1 Alex Pizzini (11306223)
A1 Yvonne Wohlfarter (8802047)
A1 Sabina Sahanic (11306226)
A1 Richard Hilbe (11306229)
A1 Benedikt Schaefer (3373130)
A1 Sonja Wagner (2960514)
A1 Felix Machleidt (11306235)
A1 Carmen Garcia (11306238)
A1 Christoph Ruwwe-Glösenkamp (11306241)
A1 Tilman Lingscheid (11306244)
A1 Laure Bosquillon de Jarcy (11306247)
A1 Miriam S. Stegemann (11965063)
A1 Moritz Pfeiffer (9187160)
A1 Linda Jürgens (11306253)
A1 Sophy Denker (494898)
A1 Daniel Zickler (3639931)
A1 Claudia Spies (11807)
A1 Andreas Edel (10703375)
A1 Nils B. Müller (11965066)
A1 Philipp Enghard (8612856)
A1 Aleksej Zelezniak (250819)
A1 Rosa Bellmann-Weiler (6731405)
A1 Günter Weiss (5649373)
A1 Archie Campbell (669891)
A1 Caroline Hayward (149856)
A1 David J. Porteous (107720)
A1 Riccardo E. Marioni (6650288)
A1 Alexander Uhrig (11306256)
A1 Heinz Zoller (132751)
A1 Judith Löffler-Ragg (5703737)
A1 Markus A. Keller (6579638)
A1 Ivan Tancevski (367877)
A1 John F. Timms (9676367)
A1 Alexey Zaikin (151304)
A1 Stefan Hippenstiel (167416)
A1 Michael Ramharter (297552)
A1 Holger Müller-Redetzky (4708141)
A1 Martin Witzenrath (167419)
A1 Norbert Suttorp (167418)
A1 Kathryn Lilley (45278)
A1 Michael Mülleder (5560511)
A1 Leif Erik Sander (9187169)
A1 Florian Kurth (350282)
A1 Markus Ralser (46782)
YR 2022
K1 Cell Biology
K1 Biotechnology
K1 Ecology
K1 Immunology
K1 Cancer
K1 Science Policy
K1 Biological Sciences not elsewhere classified
K1 showed trajectories different
K1 proteomic measurements obtained
K1 prediction model belong
K1 invasive mechanical ventilation
K1 independent validation cohort
K1 including experimental therapies
K1 grade 7 )
K1 first time point
K1 charlson comorbidity index
K1 achieved accurate classification
K1 severely ill covid
K1 required intensive care
K1 maximum treatment level
K1 proteomic survival predictor
K1 intensive care
K1 predictor trained
K1 established predictor
K1 study demonstrates
K1 severe covid
K1 prognostic discriminators
K1 optimize allocation
K1 new generation
K1 new data
K1 monitor treatment
K1 limited performance
K1 high relevance
K1 give rise
K1 complement cascade
K1 coagulation system
K1 clinical trials
K1 auroc 1
K1 auroc 0
K1 also needed
K1 additional tools
K1 81 )
K1 349 timepoints
K1 0 )
JF https://figshare.com/articles/dataset/Scripts_used_to_train_and_assess_machine_learning_models_as_well_as_the_respective_input_proteomics_data_/18644350
LK http://dx.doi.org/https://doi.org/10.1371/journal.pdig.0000007.s005
DO https://doi.org/10.1371/journal.pdig.0000007.s005
SF ELIB - SuUB Bremen

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