Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1979-1986.DOI: 10.11772/j.issn.1001-9081.2022050727
• Frontier and comprehensive applications • Previous Articles
Yi ZHANG1,2, Zhenmei WANG1()
Received:
2022-05-23
Revised:
2022-09-01
Accepted:
2022-09-05
Online:
2022-09-23
Published:
2023-06-10
Contact:
Zhenmei WANG
About author:
ZHANG Yi, born in 1977, Ph. D., professor. Her research interests include bioinformatics, machine learning, service computing.
Supported by:
通讯作者:
王真梅
作者简介:
张奕(1977—),女,江西九江人,教授,博士,主要研究方向:生物信息学、机器学习、服务计算基金资助:
CLC Number:
Yi ZHANG, Zhenmei WANG. circRNA-disease association prediction by two-stage fusion on graph auto-encoder[J]. Journal of Computer Applications, 2023, 43(6): 1979-1986.
张奕, 王真梅. 图自动编码器上二阶段融合实现的环状RNA-疾病关联预测[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1979-1986.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022050727
AUROC | AUPR | |
---|---|---|
32 | 0.902 5 | 0.137 4 |
64 | 0.916 9 | 0.175 9 |
128 | 0.924 6 | 0.216 4 |
256 | 0.9303 | 0.2271 |
512 | 0.911 6 | 0.184 8 |
Tab. 1 AUROC、AUPR values with different hidden layer dimension d
AUROC | AUPR | |
---|---|---|
32 | 0.902 5 | 0.137 4 |
64 | 0.916 9 | 0.175 9 |
128 | 0.924 6 | 0.216 4 |
256 | 0.9303 | 0.2271 |
512 | 0.911 6 | 0.184 8 |
AUROC | AUPR | |
---|---|---|
0.1 | 0.916 2 | 0.161 6 |
0.3 | 0.929 2 | 0.202 6 |
0.5 | 0.9303 | 0.2271 |
0.7 | 0.924 3 | 0.213 4 |
0.9 | 0.921 6 | 0.202 2 |
Tab. 2 AUROC、AUPR values with different balance coefficient α
AUROC | AUPR | |
---|---|---|
0.1 | 0.916 2 | 0.161 6 |
0.3 | 0.929 2 | 0.202 6 |
0.5 | 0.9303 | 0.2271 |
0.7 | 0.924 3 | 0.213 4 |
0.9 | 0.921 6 | 0.202 2 |
AUROC | AUPR | |
---|---|---|
0.001 | 0.885 5 | 0.136 0 |
0.005 | 0.921 3 | 0.184 1 |
0.01 | 0.9303 | 0.2271 |
0.05 | 0.882 0 | 0.105 2 |
0.1 | 0.736 4 | 0.043 5 |
Tab. 3 AUROC、AUPR values with different learning rate l
AUROC | AUPR | |
---|---|---|
0.001 | 0.885 5 | 0.136 0 |
0.005 | 0.921 3 | 0.184 1 |
0.01 | 0.9303 | 0.2271 |
0.05 | 0.882 0 | 0.105 2 |
0.1 | 0.736 4 | 0.043 5 |
实验分组 | 自注意力机制 | 重建损失函数 |
---|---|---|
第1组 | 引入 | 无 |
第2组 | 无 | 引入 |
第3组 | 无 | 无 |
Tab. 4 Comparison setting of ablation experiment
实验分组 | 自注意力机制 | 重建损失函数 |
---|---|---|
第1组 | 引入 | 无 |
第2组 | 无 | 引入 |
第3组 | 无 | 无 |
数据集名称 | 疾病数 | circRNA数 | 关联数 |
---|---|---|---|
circRNADisease | 34 | 223 | 241 |
circ2Disease | 46 | 215 | 240 |
circR2Disease | 88 | 585 | 650 |
Tab. 5 Data details of different datasets
数据集名称 | 疾病数 | circRNA数 | 关联数 |
---|---|---|---|
circRNADisease | 34 | 223 | 241 |
circ2Disease | 46 | 215 | 240 |
circR2Disease | 88 | 585 | 650 |
模型 | 时间/s | 模型 | 时间/s |
---|---|---|---|
GIS-CDA | 55 | RWR | 7 826 |
KATZHCDA | 8 010 | SIMCCDA | 5 |
DMFCDA | 116 |
Tab.6 Comparison of AUROC values, AUPR values and running time of the proposed model and existing models
模型 | 时间/s | 模型 | 时间/s |
---|---|---|---|
GIS-CDA | 55 | RWR | 7 826 |
KATZHCDA | 8 010 | SIMCCDA | 5 |
DMFCDA | 116 |
排名 | circRNA名称 | PMID编号 |
---|---|---|
1 | circPVT1/hsa_circ_0001821 | 31865777 |
2 | hsa_circRNA_100782/circHIPK3/ hsa_circ_0000284 | 32833501 |
3 | hsa_circ_0001313/circCCDC66 | 34252882 |
4 | circ-Foxo3/hsa_circ_0006404 | 33833780 |
5 | Cir-ITCH/hsa_circ_0001141/ hsa_circ_001763 | 29887952 |
6 | hsa_circRNA_103110/hsa_circ_103110/ hsa_circ_0004771 | 暂无证据 |
7 | CDR1as/ciRS-7/hsa_circ_0001946 | 32894144 |
8 | circPRKCI/hsa_circ_0067934 | 31409777 |
9 | hsa_circRNA_102049 | 暂无证据 |
10 | hsa_circ_0002495 | 暂无证据 |
Tab. 7 Top 10 circRNAs associated with glioma
排名 | circRNA名称 | PMID编号 |
---|---|---|
1 | circPVT1/hsa_circ_0001821 | 31865777 |
2 | hsa_circRNA_100782/circHIPK3/ hsa_circ_0000284 | 32833501 |
3 | hsa_circ_0001313/circCCDC66 | 34252882 |
4 | circ-Foxo3/hsa_circ_0006404 | 33833780 |
5 | Cir-ITCH/hsa_circ_0001141/ hsa_circ_001763 | 29887952 |
6 | hsa_circRNA_103110/hsa_circ_103110/ hsa_circ_0004771 | 暂无证据 |
7 | CDR1as/ciRS-7/hsa_circ_0001946 | 32894144 |
8 | circPRKCI/hsa_circ_0067934 | 31409777 |
9 | hsa_circRNA_102049 | 暂无证据 |
10 | hsa_circ_0002495 | 暂无证据 |
排名 | circRNA名称 | PMID编号 |
---|---|---|
1 | CirITCH/hsa_circ_0001141/ hsa_circ_001763 | 33060778 |
2 | hsa_circ_0001313/circCCDC66 | 32253030 |
3 | hsa_circ_0007534 | 32419229 |
4 | hsa_circRNA_103110/hsa_circ_103110/ hsa_circ_0004771 | 29098316 |
5 | circPRKCI/hsa_circ_0067934 | 35113408 |
6 | circ-Foxo3/hsa_circ_0006404 | 33833780 |
7 | circZFR/hsa_circRNA_103809/hsa_circ_0072088 | 32572921 |
8 | hsa_circRNA_102049 | 暂无证据 |
9 | circSMARCA5/hsa_circ_0001445 | 30956729 |
10 | circRNA_102913/hsa_circ_0058058 | 暂无证据 |
Tab. 8 Top 10 circRNAs associated with gastric cancer
排名 | circRNA名称 | PMID编号 |
---|---|---|
1 | CirITCH/hsa_circ_0001141/ hsa_circ_001763 | 33060778 |
2 | hsa_circ_0001313/circCCDC66 | 32253030 |
3 | hsa_circ_0007534 | 32419229 |
4 | hsa_circRNA_103110/hsa_circ_103110/ hsa_circ_0004771 | 29098316 |
5 | circPRKCI/hsa_circ_0067934 | 35113408 |
6 | circ-Foxo3/hsa_circ_0006404 | 33833780 |
7 | circZFR/hsa_circRNA_103809/hsa_circ_0072088 | 32572921 |
8 | hsa_circRNA_102049 | 暂无证据 |
9 | circSMARCA5/hsa_circ_0001445 | 30956729 |
10 | circRNA_102913/hsa_circ_0058058 | 暂无证据 |
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 |
[1] | Hao SUN, Jian CAO, Haisheng LI, Dianhui MAO. Session-based recommendation model based on enhanced capsule network [J]. Journal of Computer Applications, 2023, 43(4): 1043-1049. |
[2] | Yayao ZUO, Haoyu CHEN, Zhiran CHEN, Jiawei HONG, Kun CHEN. Named entity recognition method combining multiple semantic features [J]. Journal of Computer Applications, 2022, 42(7): 2001-2008. |
[3] | Wei REN, Hexiang BAI. Multi-label image classification method based on global and local label relationship [J]. Journal of Computer Applications, 2022, 42(5): 1383-1390. |
[4] | Zhihua LIU, Wenjie CHEN, Aibin CHEN. Homologous spectrogram feature fusion with self-attention mechanism for bird sound classification [J]. Journal of Computer Applications, 2022, 42(4): 1260-1268. |
[5] | Yuming ZHAO, Shenkai GU. Adversarial attack defense model with residual dense block self-attention mechanism and generative adversarial network [J]. Journal of Computer Applications, 2022, 42(3): 921-929. |
[6] | Yaming LI, Kai XING, Hongwu DENG, Zhiyong WANG, Xuan HU. Derivative-free few-shot learning based performance optimization method of pre-trained models with convolution structure [J]. Journal of Computer Applications, 2022, 42(2): 365-374. |
[7] | Rongkang XI, Manchun CAI, Tianliang LU, Yanlin LI. Tor website traffic analysis model based on self-attention mechanism and spatiotemporal features [J]. Journal of Computer Applications, 2022, 42(10): 3084-3090. |
[8] | DANG Weichao, LI Tao, BAI Shangwang, GAO Gaimei, LIU Chunxia. Real-time remaining life prediction method of Web software system based on self-attention-long short-term memory network [J]. Journal of Computer Applications, 2021, 41(8): 2346-2351. |
[9] | CHEN Jiawei, HAN Fang, WANG Zhijie. Aspect-based sentiment analysis with self-attention gated graph convolutional network [J]. Journal of Computer Applications, 2020, 40(8): 2202-2206. |
[10] | LI Shengwu, ZHANG Xuande. Multi-domain convolutional neural network based on self-attention mechanism for visual tracking [J]. Journal of Computer Applications, 2020, 40(8): 2219-2224. |
[11] | XU Yining, HE Xiaohai, ZHANG Jin, QING Linbo. Text-to-image synthesis method based on multi-level progressive resolution generative adversarial networks [J]. Journal of Computer Applications, 2020, 40(12): 3612-3617. |
[12] | ZHANG Xiaochuan, DAI Xuyao, LIU Lu, FENG Tianshuo. Chinese short text classification model with multi-head self-attention mechanism [J]. Journal of Computer Applications, 2020, 40(12): 3485-3489. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||