1 |
SALZMAN J, CHEN R E, OLSEN M N, et al. Cell-type specific features of circular RNA expression[J]. PLoS Genetics, 2013, 9(12): No.e1003777. 10.1371/journal.pgen.1003777
|
2 |
付瑶. 环状RNA——一个新的非编码RNA的功能与特性[J]. 吉林畜牧兽医, 2017, 38(11):11-13. 10.3969/j.issn.1672-2078.2017.11.003
|
|
FU Y. Function and properties of circular RNA — a new non-coding RNA[J]. Jilin Animal Husbandry and Veterinary Medicine, 2017, 38(11):11-13. 10.3969/j.issn.1672-2078.2017.11.003
|
3 |
MENG S J, ZHOU H C, FENG Z Y, et al. circRNA: functions and properties of a novel potential biomarker for cancer [J]. Molecular Cancer, 2017, 16: No.94. 10.1186/s12943-017-0663-2
|
4 |
张懿恋,张诗雨,胡吉,等. 环状RNA在糖尿病及其慢性并发症中的机制研究[J]. 中国医学科学院学报, 2022, 44(3):521-528. 10.3881/j.issn.1000-503X.13436
|
|
ZHANG Y L, ZHANG S Y, HU J, et al. Circular RNA in diabetes and its complications[J]. Acta Academiae Medicinae Sinicae, 2022, 44(3):521-528. 10.3881/j.issn.1000-503X.13436
|
5 |
FAN C Y, LEI X J, WU F X. Prediction of circRNA-disease associations using KATZ model based on heterogeneous networks[J]. International Journal of Biological Sciences, 2018, 14(14): 1950-1959. 10.7150/ijbs.28260
|
6 |
XIAO Q, YU H M, ZHONG J C, et al. An in-silico method with graph-based multi-label learning for large-scale prediction of circRNA-disease associations[J]. Genomics, 2020, 112(5): 3407-3415. 10.1016/j.ygeno.2020.06.017
|
7 |
LEI X J, FANG Z Q, CHEN L N, et al. PWCDA: path weighted method for predicting circRNA-disease associations[J]. International Journal of Molecular Sciences, 2018, 19(11): No.3410. 10.3390/ijms19113410
|
8 |
LEI X J, BIAN C. Integrating random walk with restart and k-nearest neighbor to identify novel circRNA-disease association [J]. Scientific Reports, 2020, 10: No.1943. 10.1038/s41598-020-59040-0
|
9 |
YAN C, WANG J X, WU F X. DWNN-RLS: regularized least squares method for predicting circRNA-disease associations [J]. BMC Bioinformatics, 2018, 19(S19): No.520. 10.1186/s12859-018-2522-6
|
10 |
DING Y L, CHEN B L, LEI X J, et al. Predicting novel circRNA-disease associations based on random walk and logistic regression model[J]. Computational Biology and Chemistry, 2020, 87: No.107287. 10.1016/j.compbiolchem.2020.107287
|
11 |
WANG L, YOU Z H, HUANG Y A, et al. An efficient approach based on multi-sources information to predict circRNA-disease associations using deep convolutional neural network[J]. Bioinformatics, 2020, 36(13): 4038-4046. 10.1093/bioinformatics/btz825
|
12 |
FAN C Y, LEI X J, PAN Y. Prioritizing circRNA-disease associations with convolutional neural network based on multiple similarity feature fusion[J]. Frontiers in Genetics, 2020, 11: No.540751. 10.3389/fgene.2020.540751
|
13 |
LI G H, WANG D C, ZHANG Y J, et al. Using graph attention network and graph convolutional network to explore human circrRNA-disease associations based on multi-source data[J]. Frontiers in Genetics, 2022, 13: No.829937. 10.3389/fgene.2022.829937
|
14 |
DEEPTHI K, JEREESH A S. Inferring potential circRNA-disease associations via deep autoencoder-based classification[J]. Molecular Diagnosis and Therapy, 2021, 25(1): 87-97. 10.1007/s40291-020-00499-y
|
15 |
CHEN X, WANG L, QU J, et al. Predicting miRNA-disease association based on inductive matrix completion[J]. Bioinformatics, 2018, 34(24): 4256-4265. 10.1093/bioinformatics/bty503
|
16 |
LU C Q, ZENG M, ZHANG F H, et al. Deep matrix factorization improves prediction of human circRNA-disease associations[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(3): 891-899. 10.1109/jbhi.2020.2999638
|
17 |
LI M L, LIU M Y, BIN Y N, et al. Prediction of circRNA-disease associations based on inductive matrix completion[J]. BMC Medical Genomics, 2020, 13(S5): No.42. 10.1186/s12920-020-0679-0
|
18 |
WANG D, WANG J, LU M, et al. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases [J]. Bioinformatics, 2010, 26(13): 1644-1650. 10.1093/bioinformatics/btq241
|
19 |
LI Z W, LI J S, NIE R, et al. A graph auto-encoder model for miRNA-disease associations prediction[J]. Briefings in Bioinformatics, 2021, 22(4): No.bbaa240. 10.1093/bib/bbaa240
|
20 |
LI J, ZHANG S, LIU T, et al. Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction [J]. Bioinformatics, 2020, 36(8): 2538-2546. 10.1093/bioinformatics/btz965
|
21 |
BIAN C, LEI X J, WU F X. GATCDA: predicting circRNA-disease associations based on graph attention network[J]. Cancers, 2021, 13(11): No.2595. 10.3390/cancers13112595
|
22 |
JIN C, SHI Z W, LIN K, et al. Predicting miRNA-disease association based on neural inductive matrix completion with graph autoencoders and self-attention mechanism[J]. Biomolecules, 2022, 12(1): No.64. 10.3390/biom12010064
|
23 |
FAN C Y, LEI X J, FANG Z Q, et al. circR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases [J]. Database, 2018, 2018: No.bay044. 10.1093/database/bay044
|
24 |
ZHAO Z, WANG K Y, WU F, et al. circRNA disease: a manually curated database of experimentally supported circRNA-disease associations[J]. Cell Death and Disease, 2018, 9: No.475. 10.1038/s41419-018-0503-3
|
25 |
YAO D X, ZHANG L, ZHENG M Y, et al. circ2Disease: a manually curated database of experimentally validated circRNAs in human disease [J]. Scientific Reports, 2018, 8: No.11018. 10.1038/s41598-018-29360-3
|
26 |
VURAL H, KAYA M, ALHAJJ R. A model based on random walk with restart to predict circRNA-disease associations on heterogeneous network [C]// Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: ACM, 2019: 929-932. 10.1145/3341161.3343514
|
27 |
ZHU J L, YE J L, ZHANG L, et al. Differential expression of circular RNAs in glioblastoma multiforme and its correlation with prognosis [J]. Translational Oncology, 2017, 10(2): 271-279. 10.1016/j.tranon.2016.12.006
|
28 |
DANG Y, LAN F H, OUYANG X J, et al. Expression and clinical significance of long non-coding RNA HNF1A-AS1 in human gastric cancer [J]. World Journal of Surgical Oncology, 2015, 13: No.302. 10.1186/s12957-015-0706-3
|
29 |
CHI G N, YANG F W, XU D H, et al. Silencing hsa_circ_PVT1 (circPVT1) suppresses the growth and metastasis of glioblastoma multiforme cells by up-regulation of miR-199a-5p [J]. Artificial Cells, Nanomedicine, and Biotechnology, 2020, 48(1):188-196. 10.1080/21691401.2019.1699825
|
30 |
YIN H Q CUI X. Knockdown of circHIPK3 facilitates temozolomide sensitivity in glioma by regulating cellular behaviors through miR-524-5p/KIF2A-mediated PI3K/AKT pathway[J]. Cancer Biotherapy and Radiopharmaceuticals, 2021, 36(7): 556-567. 10.1089/cbr.2020.3575
|
31 |
PENG Y, WANG H H. Cir-ITCH inhibits gastric cancer migration, invasion and proliferation by regulating the Wnt/β-catenin pathway [J]. Scientific Reports, 2020, 10: No.17443. 10.1038/s41598-020-74452-8
|
32 |
ZHANG Q, MIAO Y C, FU Q S, et al. CircRNACCDC66 regulates cisplatin resistance in gastric cancer via the miR-618/BCL2 axis[J]. Biochemical and Biophysical Research Communications, 2020, 526(3): 713-720. 10.1016/j.bbrc.2020.03.156
|