Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (7): 2125-2132.DOI: 10.11772/j.issn.1001-9081.2022060872
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Yi ZHANG1,2, Gangsheng CAI1(), Zhenmei WANG1
Received:
2022-06-16
Revised:
2022-08-22
Accepted:
2022-08-30
Online:
2022-09-22
Published:
2023-07-10
Contact:
Gangsheng CAI
About author:
ZHANG Yi, born in 1977, Ph. D., professor. Her research interests include machine learning, recommender system.Supported by:
通讯作者:
蔡钢生
作者简介:
张奕(1977—),女,江西九江人,教授,博士,主要研究方向:机器学习、推荐系统;基金资助:
CLC Number:
Yi ZHANG, Gangsheng CAI, Zhenmei WANG. Long non-coding RNA-disease association prediction model based on semantic and global dual attention mechanism[J]. Journal of Computer Applications, 2023, 43(7): 2125-2132.
张奕, 蔡钢生, 王真梅. 基于语义与全局双重注意力机制的长链非编码RNA-疾病关联预测模型[J]. 《计算机应用》唯一官方网站, 2023, 43(7): 2125-2132.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060872
元路径 | 可能代表的语义 |
---|---|
lncRNA-lncRNA | 相似的lncRNA可能存在关联 |
疾病-疾病 | 相似的疾病可能存在关联 |
lncRNA-疾病-lncRNA | 连接同一疾病的两个lncRNA可能存在关联 |
lncRNA-miRNA-lncRNA | 与同一miRNA存在交互作用的两个lncRNA可能存在关联 |
疾病-lncRNA-疾病 | 连接同一lncRNA的两个疾病可能存在关联 |
疾病-miRNA-疾病 | 连接同一miRNA的两个疾病可能存在关联 |
lncRNA-疾病 | lncRNA-疾病已知存在的关联 |
lncRNA-miRNA-疾病 | 与miRNA存在交互作用的lncRNA,可能与miRNA关联的疾病有关 |
lncRNA-疾病-miRNA-疾病 | 与疾病存在关联的lncRNA,可能与该疾病关联的miRNA所关联疾病有关 |
Tab. 1 Meta-paths for extracting semantic features and their possible semantic representations
元路径 | 可能代表的语义 |
---|---|
lncRNA-lncRNA | 相似的lncRNA可能存在关联 |
疾病-疾病 | 相似的疾病可能存在关联 |
lncRNA-疾病-lncRNA | 连接同一疾病的两个lncRNA可能存在关联 |
lncRNA-miRNA-lncRNA | 与同一miRNA存在交互作用的两个lncRNA可能存在关联 |
疾病-lncRNA-疾病 | 连接同一lncRNA的两个疾病可能存在关联 |
疾病-miRNA-疾病 | 连接同一miRNA的两个疾病可能存在关联 |
lncRNA-疾病 | lncRNA-疾病已知存在的关联 |
lncRNA-miRNA-疾病 | 与miRNA存在交互作用的lncRNA,可能与miRNA关联的疾病有关 |
lncRNA-疾病-miRNA-疾病 | 与疾病存在关联的lncRNA,可能与该疾病关联的miRNA所关联疾病有关 |
参数 | 设置 |
---|---|
近邻节点数量K | 5 |
GCN层数n | 2 |
GCN隐藏层单元数量k | 128 |
非线性变换隐藏层单元数量 | 64 |
epoch | 300 |
学习率 | 0.005 |
权重衰减系数 | 0.000 01 |
Dropout概率 | 0.4 |
Tab. 2 Setting of experimental parameters
参数 | 设置 |
---|---|
近邻节点数量K | 5 |
GCN层数n | 2 |
GCN隐藏层单元数量k | 128 |
非线性变换隐藏层单元数量 | 64 |
epoch | 300 |
学习率 | 0.005 |
权重衰减系数 | 0.000 01 |
Dropout概率 | 0.4 |
模型 | AUROC | AUPR |
---|---|---|
MFLDA | 0.740 8 ± / | 0.204 5 ± 0.002 3 |
TPGLDA | 0.877 1 ± 0.005 3 | 0.319 2 ± 0.005 8 |
GCNLDA | 0.9598 ± / | 0.223 3 ± / |
VGAELDA | 0.969 2 ± 0.008 0 | 0.820 3 ± 0.013 9 |
SGALDA | 0.9945 ± 0.000 2 | 0.9167 ± 0.001 1 |
Tab. 3 Experimental results
模型 | AUROC | AUPR |
---|---|---|
MFLDA | 0.740 8 ± / | 0.204 5 ± 0.002 3 |
TPGLDA | 0.877 1 ± 0.005 3 | 0.319 2 ± 0.005 8 |
GCNLDA | 0.9598 ± / | 0.223 3 ± / |
VGAELDA | 0.969 2 ± 0.008 0 | 0.820 3 ± 0.013 9 |
SGALDA | 0.9945 ± 0.000 2 | 0.9167 ± 0.001 1 |
模型 | 邻域特征提取 | 语义特征提取 | 双重注意力机制 | |
---|---|---|---|---|
语义 | 全局 | |||
SGALDA-neighbor | √ | |||
SGALDA-avgsemantic | √ | 平均 | ||
SGALDA-semantic | √ | √ | ||
SGALDA-ns | √ | √ | 平均 | 平均 |
SGALDA-ns-s | √ | √ | √ | 平均 |
SGALDA-ns-g | √ | √ | 平均 | √ |
SGALDA | √ | √ | √ | √ |
Tab. 4 Module usage of SGALDA and its variants
模型 | 邻域特征提取 | 语义特征提取 | 双重注意力机制 | |
---|---|---|---|---|
语义 | 全局 | |||
SGALDA-neighbor | √ | |||
SGALDA-avgsemantic | √ | 平均 | ||
SGALDA-semantic | √ | √ | ||
SGALDA-ns | √ | √ | 平均 | 平均 |
SGALDA-ns-s | √ | √ | √ | 平均 |
SGALDA-ns-g | √ | √ | 平均 | √ |
SGALDA | √ | √ | √ | √ |
模型 | AUROC | AUPR |
---|---|---|
SGALDA-avgsemantic | 0.974 5 ± 0.000 2 | 0.736 4 ± 0.001 1 |
SGALDA-semantic | 0.971 4 ± 0.002 2 | 0.784 8 ± 0.004 6 |
SGALDA-ns | 0.992 6 ± 0.000 2 | 0.897 3 ± 0.002 1 |
SGALDA-ns-s | 0.992 7 ± 0.000 1 | 0.898 9 ± 0.001 8 |
SGALDA-neighbor | 0.993 2 ± 0.000 2 | 0.899 7 ± 0.001 5 |
SGALDA-ns-g | 0.992 9 ± 0.000 3 | 0.900 5 ± 0.002 1 |
SGALDA | 0.9945 ± 0.000 2 | 0.9167 ± 0.001 1 |
Tab. 5 Comparison results of SGALDA and its variants
模型 | AUROC | AUPR |
---|---|---|
SGALDA-avgsemantic | 0.974 5 ± 0.000 2 | 0.736 4 ± 0.001 1 |
SGALDA-semantic | 0.971 4 ± 0.002 2 | 0.784 8 ± 0.004 6 |
SGALDA-ns | 0.992 6 ± 0.000 2 | 0.897 3 ± 0.002 1 |
SGALDA-ns-s | 0.992 7 ± 0.000 1 | 0.898 9 ± 0.001 8 |
SGALDA-neighbor | 0.993 2 ± 0.000 2 | 0.899 7 ± 0.001 5 |
SGALDA-ns-g | 0.992 9 ± 0.000 3 | 0.900 5 ± 0.002 1 |
SGALDA | 0.9945 ± 0.000 2 | 0.9167 ± 0.001 1 |
疾病 | 排名 | lncRNA名称 | PMID |
---|---|---|---|
乳腺癌 | 1 | MIR17HG | 25680407 |
2 | MYCNOS | 未确认 | |
3 | CASC15 | 33551326; 35235236 | |
4 | HAND2-AS1 | 33290256; 33015182 | |
5 | LINC00467 | 33996559; 34956437 | |
6 | TUG1 | 29657297; 28950664 | |
7 | LINC00261 | 33274565; 32440206 | |
8 | HNF1A-AS1 | 32319789; 35539662 | |
9 | PCAT5 | 未确认 | |
10 | LINC00963 | 32052688 | |
胃癌 | 1 | MALAT1 | 34036905; 28077118 |
2 | NEAT1 | 33982777; 29363783 | |
3 | CCAT2 | 25755774; 28248065 | |
4 | MIR17HG | 31186404 | |
5 | DISC2 | 未确认 | |
6 | KCNQ1OT1 | 33682985; 31915311 | |
7 | LINC00312 | 未确认 | |
8 | HULC | 31726371 | |
9 | TUG1 | 34762771 | |
10 | BCYRN1 | 31652309 |
Tab. 6 LncRNAs potentially associated with breast cancer and stomach cancer
疾病 | 排名 | lncRNA名称 | PMID |
---|---|---|---|
乳腺癌 | 1 | MIR17HG | 25680407 |
2 | MYCNOS | 未确认 | |
3 | CASC15 | 33551326; 35235236 | |
4 | HAND2-AS1 | 33290256; 33015182 | |
5 | LINC00467 | 33996559; 34956437 | |
6 | TUG1 | 29657297; 28950664 | |
7 | LINC00261 | 33274565; 32440206 | |
8 | HNF1A-AS1 | 32319789; 35539662 | |
9 | PCAT5 | 未确认 | |
10 | LINC00963 | 32052688 | |
胃癌 | 1 | MALAT1 | 34036905; 28077118 |
2 | NEAT1 | 33982777; 29363783 | |
3 | CCAT2 | 25755774; 28248065 | |
4 | MIR17HG | 31186404 | |
5 | DISC2 | 未确认 | |
6 | KCNQ1OT1 | 33682985; 31915311 | |
7 | LINC00312 | 未确认 | |
8 | HULC | 31726371 | |
9 | TUG1 | 34762771 | |
10 | BCYRN1 | 31652309 |
1 | 王馨悦,李剑.长链非编码RNA在肿瘤中的研究进展[J].癌症进展, 2018, 16(11): 1328-1330, 1423. |
WANG X Y, LI J. Research advances of lncRNA in tumors[J]. Oncology Progress, 2018, 16(11): 1328-1330, 1423. | |
2 | GEISLER S, COLLER J. RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts[J]. Nature Reviews Molecular Cell Biology, 2013, 14(11): 699-712. 10.1038/nrm3679 |
3 | 彭颖,冯继锋.长链非编码RNA在常见肿瘤中的研究进展[J].中国肿瘤外科杂志, 2020, 12(2): 174-179, 184. 10.3969/j.issn.1674-4136.2020.02.019 |
PENG Y, FENG J F. Research progress of long non-coding RNA in common tumors[J]. Chinese Journal of Surgical Oncology, 2020, 12(2): 174-179, 184. 10.3969/j.issn.1674-4136.2020.02.019 | |
4 | YAN C C, ZHANG Z C, BAO S Q, et al. Computational methods and applications for identifying disease-associated lncRNAs as potential biomarkers and therapeutic targets[J]. Molecular Therapy — Nucleic Acids, 2020, 21: 156-171. 10.1016/j.omtn.2020.05.018 |
5 | CHEN G, WANG Z Y, WANG D Q, et al. LncRNADisease: a database for long-non-coding RNA-associated diseases[J]. Nucleic Acids Research, 2013, 41(D1): D983-D986. 10.1093/nar/gks1099 |
6 | NING S W, ZHANG J Z, WANG P, et al. Lnc2Cancer: a manually curated database of experimentally supported lncRNAs associated with various human cancers[J]. Nucleic Acids Research, 2016, 44(D1): D980-D985. 10.1093/nar/gkv1094 |
7 | LI Y, QIU C X, TU J, et al. HMDD v2.0: a database for experimentally supported human microRNA and disease associations[J]. Nucleic Acids Research, 2014, 42(D1): D1070-D1074. 10.1093/nar/gkt1023 |
8 | WANG Y T, JUAN L R, PENG J J, et al. LncDisAP: a computation model for LncRNA-disease association prediction based on multiple biological datasets[J]. BMC Bioinformatics, 2019, 20(S16): No.582. 10.1186/s12859-019-3081-1 |
9 | LI J C, ZHAO H C, XUAN Z W, et al. A novel approach for potential human lncRNA-disease association prediction based on local random walk[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, 18(3): 1049-1059. 10.1109/tcbb.2019.2934958 |
10 | ZHANG J P, ZHANG Z P, CHEN Z G, et al. Integrating multiple heterogeneous networks for novel lncRNA-disease association inference[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 16(2): 396-406. 10.1109/tcbb.2017.2701379 |
11 | DING L, WANG M H, SUN D D, et al. TPGLDA: novel prediction of associations between lncRNAs and diseases via lncRNA-disease-gene tripartite graph[J]. Scientific Reports, 2018, 8: No.1065. 10.1038/s41598-018-19357-3 |
12 | FU G Y, WANG J, DOMENICONI C, et al. Matrix factorization-based data fusion for the prediction of lncRNA-disease associations[J]. Bioinformatics, 2018, 34(9): 1529-1537. 10.1093/bioinformatics/btx794 |
13 | ZENG M, LU C Q, ZHANG F H, et al. SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning[J]. Methods, 2020, 179: 73-80. 10.1016/j.ymeth.2020.05.002 |
14 | LU C Q, YANG M Y, LI M, et al. Predicting human lncRNA-disease associations based on geometric matrix completion[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(8): 2420-2429. 10.1109/jbhi.2019.2958389 |
15 | XUAN P, PAN S X, ZHANG T G, et al. Graph convolutional network and convolutional neural network based method for predicting lncRNA-disease associations[J]. Cells, 2019, 8(9): No.1012. 10.3390/cells8091012 |
16 | SHI Z W, ZHANG H, JIN C, et al. A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations[J]. BMC Bioinformatics, 2021, 22: No.136. 10.1186/s12859-021-04073-z |
17 | WANG L, ZHONG C. gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network[J]. BMC Bioinformatics, 2022, 23: No.11. 10.1186/s12859-021-04548-z |
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 | CHEN X, YAN G Y. Novel human lncRNA-disease association inference based on lncRNA expression profiles[J]. Bioinformatics, 2013, 29(20): 2617-2624. 10.1093/bioinformatics/btt426 |
20 | LU M, ZHANG Q P, DENG M, et al. An analysis of human microRNA and disease associations[J]. PLoS ONE, 2008, 3(10): No.e3420. 10.1371/journal.pone.0003420 |
21 | CHEN X, CLARENCE YAN C G, LUO C, et al. Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity[J]. Scientific Reports, 2015, 5: No.11338. 10.1038/srep11338 |
22 | KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. (2017-02-22) [2022-05-03]. . 10.48550/arXiv.1609.02907 |
23 | NAIR V, HINTON G E. Rectified linear units improve restricted Boltzmann machines [C]// Proceedings of the 27th International Conference on Machine Learning. Madison, WI: Omnipress, 2010: 807-814. |
24 | CAI L J, LU C C, XU J L, et al. Drug repositioning based on the heterogeneous information fusion graph convolutional network[J]. Briefings in Bioinformatics, 2021, 22(6): No.bbab319. 10.1093/bib/bbab319 |
25 | LI Q M, HAN Z C, WU X M. Deeper insights into graph convolutional networks for semi-supervised learning [C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 3538-3545. 10.1609/aaai.v32i1.11604 |
26 | LI X, DING D H, KAO B, et al. Leveraging meta-path contexts for classification in heterogeneous information networks [C]// Proceedings of the IEEE 37th International Conference on Data Engineering. Piscataway: IEEE, 2021: 912-923. 10.1109/icde51399.2021.00084 |
27 | KINGMA D P, BA J L. Adam: a method for stochastic optimization[EB/OL]. (2017-01-30) [2022-05-16]. . |
28 | SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15: 1929-1958. |
29 | LU Z Y, COHEN K B, HUNTER L. GeneRIF quality assurance as summary revision [C]// Proceedings of the Pacific Symposium on Biocomputing 2007. Maui: World Scientific Publishing, 2007: 269-280. 10.1142/9789812772435_0026 |
30 | LI J H, LIU S, ZHOU H, et al. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data[J]. Nucleic Acids Research, 2014, 42(D1): D92-D97. 10.1093/nar/gkt1248 |
31 | 刘宗超,李哲轩,张阳,等. 2020全球癌症统计报告解读[J].肿瘤综合治疗电子杂志, 2021, 7(2): 1-14. |
LIU Z C, LI Z X, ZHANG Y, et al. Interpretation on the report of global cancer statistics 2020[J]. Journal of Multidisciplinary Cancer Management (Electronic Version), 2021, 7(2): 1-14. | |
32 | CHACON-CORTES D, SMITH R A, LEA R A, et al. Association of microRNA 17-92 cluster Host Gene (MIR17HG) polymorphisms with breast cancer[J]. Tumor Biology, 2015, 36(7): 5369-5376. 10.1007/s13277-015-3200-1 |
33 | SHEN P, YU Y, YAN Y, et al. LncRNA CASC15 regulates breast cancer cell stemness via the miR-654-5p/MEF2D axis[J]. Journal of Biochemical and Molecular Toxicology, 2022, 36(5): No.e23023. 10.1002/jbt.23023 |
34 | DONG G L, WANG X R, JIA Y, et al. HAND2-AS1 works as a ceRNA of miR-3118 to suppress proliferation and migration in breast cancer by upregulating PHLPP2[J]. BioMed Research International, 2020, 2020: No.8124570. 10.1155/2020/8124570 |
35 | XU W, DING M D, WANG B, et al. Molecular mechanism of the canonical oncogenic lncRNA MALAT1 in gastric cancer[J]. Current Medicinal Chemistry, 2021, 28(42): 8800-8809. 10.2174/1875533xmte1anzi90 |
36 | ZHOU Y, SHA Z H, YANG Y, et al. lncRNA NEAT1 regulates gastric carcinoma cell proliferation, invasion and apoptosis via the miR‑500a‑3p/XBP‑1 axis[J]. Molecular Medicine Reports, 2021, 24(1): No.503. 10.3892/mmr.2021.12142 |
37 | FENG L, LI H Q, LI F, et al. LncRNA KCNQ1OT1 regulates microRNA-9-LMX1A expression and inhibits gastric cancer cell progression[J]. Aging, 2020, 12(1): 707-717. 10.18632/aging.102651 |
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