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2,351
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Scgnn is a novel graph neural network framework for single-cell rna-seq analyses
Author(s):
J. Wang
,
A. MA
,
Y. CHANG
,
J Wang
,
Y Chang
,
J GONG
,
Y JIANG
,
R Qi
,
C. WANG
,
H Fu
,
Q Ma
,
D. XU
,
J Gong
,
Y Jiang
,
R QI
,
A. Ma
,
S Fan
,
Y XU
,
Y LU
,
Q. ZHang
,
J Zhou
,
RH Fang
,
W. GAO
,
L Zhang
,
JJA van Kampen
,
H-Y Huang
,
M. Broughton
,
M MOHSENI
,
R. Babbush
,
S. Boixo
,
H. Neven
,
JR McClean
,
MA Stoffel
,
SE Johnston
,
JG Pilkington
,
JM Pemberton
,
Z Li
,
S. ichiro Hattori,
,
G. Forzieri
,
Y. Wang
,
C. TONG
,
H. Dai
,
Z Wu
,
X Han
,
Y GUO
,
S-I Hattori
,
N. Higashi-Kuwata
,
H HAYASHI
,
S.R. Allu
,
J. Raghavaiah
,
H. Bulut
,
D Das
,
B.J. Anson
,
E.K. Lendy
,
Y Takamatsu
,
D. Debananda
Publication date:
2021
Journal:
Nat. Commun
Read this article at
ScienceOpen
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PubMed
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There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
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Author and article information
Journal
PubMed ID::
33397941
DOI::
10.1038/s41467-021-20900-6
ScienceOpen disciplines:
Bioinformatics & Computational biology
Data availability:
ScienceOpen disciplines:
Bioinformatics & Computational biology
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