He, X.,
Wang, Z.,
Li, Y.,
Khazhina, S.,
Du, W.,
Wang, J.,
Wang, W. (2022) accurate than three common methods (Encoder-Decoder Recurrent
Neural Network, Bidirectional Long Short
Bo, C.,
Zhang, H.,
Cao, Y.,
Lu, X.,
Zhang, C.,
Li, S.,
Kong, X.,
Zhang, X.,
Bai, M.,
Tian, K.,
Saitgareeva, A.,
Lyaysan, G.,
Wang, J.,
Ning, S.,
Wang, L. (2021) of transcription factors (TFs) and the relationship among them remain unclear. A TF–miRNA–gene
network (TMGN) of MG
LIU, Z.,
ZHANG, R.,
CHEN, X.,
YAO, P.,
YAN, T.,
LIU, W.,
YAO, J.,
ZHAO, S.,
SOKHATSKII, A.,
GAREEV, I. (2019) through PPI
network analysis were verified in the rat model of ICH. In addition, we obtained three small
Chu, Jiawei,
Kan, Xiu,
Che, Yan,
Song, Wanqing,
Aleksey, Kudreyko,
Dong, Zhengyuan (2024) Chinese electronic medical records, thereby enhancing the data
analysis capabilities of rehabilitation