Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Julya S. Kisrieva"'
Autor:
Natalia A Petushkova, Mikhail A Pyatnitskiy, Vladislav A Rudenko, Olesya V Larina, Oxana P Trifonova, Julya S Kisrieva, Natalia F Samenkova, Galina P Kuznetsova, Irina I Karuzina, Andrey V Lisitsa
Publikováno v:
PLoS ONE, Vol 9, Iss 8, p e103950 (2014)
BackgroundThere are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the trainin
Externí odkaz:
https://doaj.org/article/19e68aad4d1042b8af826bda1c4f9e10
Autor:
Julya S. Kisrieva, V. N. Kashirtseva, A. V. Lisitsa, Oxana P. Trifonova, N. F. Samenkova, A S Chernobrovkin, N. A. Petushkova, O. V. Larina, N. F. Belayeva, G P Kuznetsova, I. I. Karuzina
Publikováno v:
Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry. 6:23-30
Using one-dimensional proteomic mapping (combination of one-dimensional gel electrophoresis (1DE) with subsequent mass spectrometry MALDI-TOF-PMF) the protein profile of Danio rerio embryos has been investigated. The fish species Danio rerio is the m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::712f9d78296bdae7f79270e46fb3f463
https://doi.org/10.1134/s1990750812010040
https://doi.org/10.1134/s1990750812010040
Autor:
V N Kashirceva, A. V. Lisitsa, N. A. Petushkova, A S Chernobrovkin, N. F. Samenkova, O B Larina, Julya S. Kisrieva, G P Kuznetsova, I. I. Karuzina, N. F. Belayeva, Oxana P. Trifonova
Publikováno v:
Biomeditsinskaya Khimiya. 57:593-603
In the present study, a proteomic technology combining one-dimensional gel electrophoresis (1DE) with subsequent mass spectrometry (MALDI-TOF-PMF) has been successfully applied for revelation of changes in the protein profile of zebrafish (Danio reri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac5af03c40b04dc23f500da8f72f5670
https://doi.org/10.18097/pbmc20115706593
https://doi.org/10.18097/pbmc20115706593
Autor:
Oxana P. Trifonova, O. V. Larina, Julya S. Kisrieva, N. F. Samenkova, N. A. Petushkova, Andrey Lisitsa, Mikhail A. Pyatnitskiy, G P Kuznetsova, I. I. Karuzina, Vladislav A. Rudenko
Publikováno v:
PLoS ONE, Vol 9, Iss 8, p e103950 (2014)
PLoS ONE
PLoS ONE
BackgroundThere are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the trainin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ad28480f4dbb6116c12adb23bed3840
https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25083712/?tool=EBI
https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25083712/?tool=EBI