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1 Ergebnisse
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Machine learning approaches to predict gestational age in n..:
Yamauchi, Takafumi
;
Ochi, Daisuke
;
Matsukawa, Naomi
...
Scientific Reports. 11 (2021) 1 - p. , 2021
Link:
https://doi.org/10.1038/s41598-021-97342-z
RT Journal T1
Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
UL https://suche.suub.uni-bremen.de/peid=cr-10.1038_s41598-021-97342-z&Exemplar=1&LAN=DE A1 Yamauchi, Takafumi A1 Ochi, Daisuke A1 Matsukawa, Naomi A1 Saigusa, Daisuke A1 Ishikuro, Mami A1 Obara, Taku A1 Tsunemoto, Yoshiki A1 Kumatani, Satsuki A1 Yamashita, Riu A1 Tanabe, Osamu A1 Minegishi, Naoko A1 Koshiba, Seizo A1 Metoki, Hirohito A1 Kuriyama, Shinichi A1 Yaegashi, Nobuo A1 Yamamoto, Masayuki A1 Nagasaki, Masao A1 Hiyama, Satoshi A1 Sugawara, Junichi PB Springer Science and Business Media LLC YR 2021 SN 2045-2322 JF Scientific Reports VO 11 IS 1 LK http://dx.doi.org/https://doi.org/10.1038/s41598-021-97342-z DO https://doi.org/10.1038/s41598-021-97342-z SF ELIB - SuUB Bremen
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