LIU, Z.,
WANG, X.,
YANG, G.,
ZHONG, C.,
ZHANG, R.,
YE, J.,
ZHONG, Y.,
HU, J.,
OZAL, B.,
ZHAO, S. (2020) -related
ceRNA regulatory
network, in glioblastoma (GBM) has not been fully elucidated. The goal
Jiang, Jianhao,
Gareev, Ilgiz,
Ilyasova, Tatiana,
Shumadalova, Alina,
Du, Weijie,
Yang, Baofeng (2024) -established
ceRNA networks, involving classical interactions between lncRNAs, microRNAs (miRNAs), and messenger
Xu, Dongxiao,
Gareev, Ilgiz,
Beylerli, Ozal,
Pavlov, Valentin,
Le, Huang,
Shi, Huaizhang (2024) differentially expressed mRNAs (DEGs) were pinpointed concerning IA. Subsequently, a mi
RNA-mRNA network for delving the molecular mechanism of DEGs, and protein–protein interaction (PPI)
networks and micro
RNA (mi
RNA RNAs (tRNAs) on their processing to form small non-coding RNAs. This is evidenced by the use of t
RNABo, 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–mi
RNA–gene
network (TMGN) of MG
Gilyazova, I.,
Ivanova, E.,
Pavlov, V.,
Khasanova, G.,
Khasanova, A.,
Izmailov, A.,
Asadullina, D.,
Gilyazova, G.,
Wang, G.,
Gareev, I.,
Beylerli, O. (2023) of this study was to
analyze the expression level of mi
RNA-146a, mi
RNA-126, mi
RNA-218, mi
RNA-410, mi
RNA-503
of the reliability analysis of power distribution
networks. With the help of deep learning, which has
Длинные некодирующие РНК: какие перспективы? conditions, including cancer. The perception of lncRNAs as fragments of
RNA and transcriptional noise has