Open Access
Survey/Review Study
Network Learning for Biomarker Discovery
Yulian Ding1
, Minghan Fu1
, Ping Luo2
, Fang-Xiang Wu1, 3, 4, *
Author Information
Submitted: 14 Oct 2022 | Accepted: 5 Dec 2022 | Published: 27 Mar 2023
Abstract
Everything is connected and thus networks are instrumental in not only modeling complex systems with many components, but also accommodating knowledge about their components. Broadly speaking, network learning is an emerging area of machine learning to discover knowledge within networks. Although networks have permeated all subjects of sciences, in this study we mainly focus on network learning for biomarker discovery. We first overview methods for traditional network learning which learn knowledge from networks with centrality analysis. Then, we summarize the network deep learning, which are powerful machine learning models that integrate networks (graphs) with deep neural networks. Biomarkers can be placed in proper biological networks as vertices or edges and network learning applications for biomarker discovery are discussed. We finally point out some promising directions for future work about network learning.
Graphical Abstract
Keywords
References
1.
Fournier, J.C. Graphs Theory and Applications: With Exercises and Problems; Wiley-ISTE: London, 2009.
2.
Li, A.S.; Li, J.K.; Pan, Y.C.; et al. Homophyly/kinship model: Naturally evolving networks. Sci. Rep., 2015, 5: 15140.
3.
Chen, B.L.; Wang, J.X.; Li, M.; et al. Identifying disease genes by integrating multiple data sources. BMC Med. Genomics, 2014, 7: S2.
4.
Li, Y.F.; Wu, F.X.; Ngom, A. A review on machine learning principles for multi-view biological data integration. Briefings Bioinf., 2018, 19: 325−340.
5.
Gentili, M.; Martini, L.; Sponziello, M.; et al. Biological random walks: Multi-omics integration for disease gene prioritization. Bioinformatics, 2022, 38: 4145−4152.
6.
Lewis, T.G. Network Science: Theory and Applications; John Wiley & Sons, Inc.: New Jersey, 2009.
7.
Težak, Ž.; Kondratovich, M.V.; Mansfield, E. Us FDA and personalized medicine: In vitro diagnostic regulatory perspective. Pers. Med., 2019, 7: 517−530.
8.
OMIM. An Online Catalog of Human Genes and Genetic Disorders. Available online: https://omim.org/ (accessed on 12 October 2022).
9.
Parameswaran, S.; Kundapur, D.; Vizeacoumar, F.S.; et al. A road map to personalizing targeted cancer therapies using synthetic lethality. Trends Cancer, 2019, 5: 11−29.
10.
Goh, K.I.; Cusick, M.E.; Valle, D.; et al. The human disease network. Proc. Natl. Acad. Sci. USA, 2007, 104: 8685−8690.
11.
Vidal, M.; Cusick, M.E.; Barabási, A.L. Interactome networks and human disease. Cell, 2011, 144: 986−998.
12.
Oti, M.; Brunner, H.G. The modular nature of genetic diseases. Clin Genet, 2007, 71: 1−11.
13.
Yıldırım, M.A.; Goh, K.I.; Cusick, M.E.; et al. Drug-target network. Nat Biotechnol, 2007, 25: 1119−1126.
14.
Bullmore, E.; Sporns, O. Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci., 2009, 10: 186−198.
15.
Jones, S.; Thornton, J.M. Principles of protein-protein interactions. Proc. Natl. Acad. Sci. USA, 1996, 93: 13−20.
16.
Venkatesan, K.; Rual, J.F.; Vazquez, A.; et al. An empirical framework for binary interactome mapping. Nat. Methods, 2009, 6: 83−90.
17.
Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; et al. Drugbank 5.0: A major update to the drugbank database for 2018. Nucleic Acids Res., 2018, 46: D1074−D1082.
18.
Ideker, T.; Krogan, N.J. Differential network biology. Mol. Syst. Biol., 2012, 8: 565.
19.
Oughtred, R.; Stark, C.; Breitkreutz, B.J.; et al. The biogrid interaction database: 2019 update. Nucleic Acids Res., 2019, 47: D529−D541.
20.
Gysi, D.M.; do Valle, Í.; Zitnik, M.; et al. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc. Natl. Acad. Sci. USA, 2021, 118: e2025581118.
21.
Guney, E.; Menche, J.; Vidal, M.; et al. Network-based in silico drug efficacy screening. Nat. Commun., 2016, 7: 10331.
22.
Brandes, U.; Erlebach, T. Network Analysis: Methodological Foundations; Springer: Berlin, Heidelberg, 2005. doi: 10.1007/b106453
23.
Latora, V.; Nicosia, V.; Russo, G. Complex Networks: Principles, Methods and Applications; Cambridge University Press: Cambridge, 2017.
24.
Liu, Y.Y.; Slotine, J.J.; Barabási, A.L. Control centrality and hierarchical structure in complex networks. PLoS One, 2012, 7: e44459.
25.
Wu, L.; Li, M.; Wang, J.X.; et al. Cytoctrlanalyser: A cytoscape app for biomolecular network controllability analysis. Bioinformatics, 2018, 38: 1428−1430.
26.
Liu, Y.Y.; Slotine, J.J.; Barabási, A.L. Controllability of complex networks. Nature, 2011, 473: 167−173.
27.
Göbel, F.; Jagers, A.A. Random walks on graphs. Stoch. Process. Their Appl., 1974, 2: 311−336.
28.
Grover, A.; Leskovec, J. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 13 August 2016; ACM: San Francisco, 2016; pp. 855–864. doi: 10.1145/2939672.2939754
29.
Jeong, H.; Mason, S.P.; Barabási, A.L.; et al. Lethality and centrality in protein networks. Nature, 2001, 411: 41−42.
30.
Han, J.D.J.; Bertin, N.; Hao, T.; et al. Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature, 2004, 430: 88−93.
31.
Wu, X.B.; Jiang, R.; Zhang, M.Q.; et al. Network-based global inference of human disease genes. Mol. Syst. Biol., 2008, 4: 189.
32.
Ramirez, F.; Schlicker, A.; Assenov, Y.; et al. Computational analysis of human protein interaction networks. Proteomics, 2007, 7: 2541−2552.
33.
Peng, W.; Wang, J.X.; Wang, W.P.; et al. Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks. BMC Syst. Biol., 2012, 6: 87.
34.
Li, M.; Zhang, H.H.; Wang, J.X.; et al. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. BMC Syst. Biol., 2012, 6: 15.
35.
Mistry, D.; Wise, R.P.; Dickerson, J.A. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network. PLoS One, 2017, 12: e0187091.
36.
Elahi, A.; Babamir, S.M. Identification of essential proteins based on a new combination of topological and biological features in weighted protein–protein interaction networks. IET Syst. Biol., 2018, 12: 247−257.
37.
Junker, B.H.; Koschützki, D.; Schreiber, F. Exploration of biological network centralities with centiBiN. BMC Bioinformatics, 2006, 7: 219.
38.
Grassler, J.; Koschützki, D.; Schreiber, F. CentiLiB: Comprehensive analysis and exploration of network centralities. Bioinformatics, 2012, 28: 1178−1179.
39.
Tang, Y.; Li, M.; Wang, J.X.; et al. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems, 2015, 127: 67−72.
40.
Özgür, A.; Vu, T.; Erkan, G.; et al. Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics, 2008, 24: i277−i285.
41.
Wei, P.J.; Wu, F.X.; Xia, J.F.; et al. Prioritizing cancer genes based on an improved random walk method. Front. Genet., 2020, 11: 377.
42.
Li, Y.J.; Li, J.Y. Disease gene identification by random walk on multigraphs merging heterogeneous genomic and phenotype data. BMC Genomics, 2012, 13: S27.
43.
Ding, Y.L.; Chen, B.L.; Lei, X.J.; et al. Predicting novel CircRNA-disease associations based on random walk and logistic regression model. Comput. Biol. Chem., 2020, 87: 107287.
44.
Chen, X.; Liu, M.X.; Yang, G.Y. RWRMDA: Predicting novel human microRNA-disease associations. Mol. BioSyst., 2012, 8: 2792−2798.
45.
Ashburn, T.T.; Thor, K.B. Drug repositioning: Identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov., 2004, 3: 673−683.
46.
Lamb, J.; Crawford, E.D.; Peck, D.; et al. The connectivity map: Using gene-expression signatures to connect small molecules, genes, and disease. Science, 2006, 313: 1929−1935.
47.
Fei, W.; Lei, X.J.; Wu, F.X. A review of drug repositioning based chemical-induced cell line expression data. Curr. Med. Chem., 2020, 27: 5340−5350.
48.
Wang, F.; Ding, Y.L.; Lei, X.J.; et al. Identifying gene signatures for cancer drug repositioning based on sample clustering. IEEE/ACM Trans. Comput. Biol. Bioinf., 2022, 19: 953−965.
49.
Huang, C.H.; Chang, P.M.H.; Hsu, C.W.; et al. Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory. BMC Bioinformatics, 2016, 17: S2.
50.
Wu, F.X.; Wu, L.; Wang, J.X.; et al. Transittability of complex networks and its applications to regulatory biomolecular networks. Sci. Rep., 2014, 4: 4819.
51.
Wu, L.; Li, M.; Wang, J.X.; et al. Controllability and its applications to biological networks. J. Comput. Sci. Technol., 2019, 34: 16−34.
52.
Wu, L.; Tang, L.K.; Li, M.; et al. Biomolecular network controllability with drug binding information. IEEE Trans. NanoBiosci., 2017, 16: 326−332.
53.
Wu, L.; Shen, Y.C.; Li, M.; et al. Network output controllability-based method for drug target identification. IEEE Trans. NanoBiosci, 2015, 14: 184−191.
54.
Rudie, J.D.; Brown, J.A.; Beck-Pancer, D.; et al. Altered functional and structural brain network organization in autism. NeuroImage Clin., 2013, 2: 79−94.
55.
Wang, D.H.; Buckner, R.L.; Fox, M.D.; et al. Parcellating cortical functional networks in individuals. Nat. Neurosci., 2015, 18: 1853−1860.
56.
Power, J.D.; Cohen, A.L.; Nelson, S.M.; et al. Functional network organization of the human brain. Neuron, 2011, 72: 665−678.
57.
Liu, J.; Li, M.; Pan, Y.; et al. Complex brain network analysis and its applications to brain disorders: A survey. Complexity, 2017, 2017: 8362741.
58.
Hagmann, P.; Cammoun, L.; Gigandet, X.; et al. Mapping the structural core of human cerebral cortex. PLoS Biol., 2008, 6: e159.
59.
Bai, F.; Shu, N.; Yuan, Y.G.; et al. Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. J. Neurosci., 2012, 32: 4307−4318.
60.
Gu, S.; Pasqualetti, F.; Cieslak, M.; et al. Controllability of structural brain networks. Nat. Commun., 2015, 6: 8414.
61.
Power, J.D.; Cohen, A.L.; Nelson, S.M.; et al. Functional network organization of the human brain. Neuron, 2011, 72: 665−678.
62.
Mostafa, S.; Tang, L.K.; Wu, F.X. Diagnosis of autism spectrum disorder based on eigenvalues of brain networks. IEEE Access, 2019, 7: 128474−128486.
63.
Yin, W.T.; Mostafa, S.; Wu, F.X. Diagnosis of autism spectrum disorder based on functional brain networks with deep learning. J. Comput. Biol., 2021, 28: 146−615.
64.
Luo, P.; Ding, Y.L.; Lei, X.J.; et al. deepDriver: Predicting cancer driver genes based on somatic mutations using deep convolutional neural networks. Front. Genet., 2019, 10: 13.
65.
Zhang, S.; Tong, H.H.; Xu, J.J.; et al. Graph convolutional networks: A comprehensive review. Comput. Soc. Netw., 2019, 6: 11.
66.
Wu, Z.H.; Pan, S.R.; Chen, F.W.; et al. A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst., 2021, 32: 4−24.
67.
Duvenaud, D.; Maclaurin, D.; Aguilera-Iparraguirre, J.; et al. Convolutional networks on graphs for learning molecular fingerprints. In Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, 07 December 2015; MIT Press: Montreal, 2015; pp. 2224–2232. doi: 10.5555/2969442.2969488
68.
Hamilton, W.L.; Ying, R.; Leskovec, J. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, 04 December 2017; Curran Associates Inc.: Long Beach, 2017; pp. 1025–1035. doi: 10.5555/3294771.3294869
69.
Zhang, Y.C.; Lei, X.J.; Pan, Y.; et al. Drug repositioning with graphSAGE and clustering constraints based on drug and disease networks. Front. Pharmacol., 2022, 13: 872785.
70.
Aggarwal, C.C. Neural Networks and Deep Learning: A Textbook; Springer: Cham, Switzerland, 2018. doi: 10.1007/978-3-319-94463-0
71.
Kipf, T.N.; Welling, M. Variational graph auto-encoders. arXiv: 1611.07308, 2016. Available online: https://arxiv.org/abs/1611.07308(accessed on 11 October 2022).
72.
Singh, V.; Lio’, P. Towards probabilistic generative models harnessing graph neural networks for disease-gene prediction. arXiv: 1907.05628, 2019. Available online: https://arxiv.org/abs/1907.05628v1(accessed on 12 October 2022).
73.
Wang, X.C.; Gong, Y.C.; Yi, J.; et al. Predicting gene-disease associations from the heterogeneous network using graph embedding. In Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine, San Diego, 18–21 November 2019; IEEE: San Diego, 2019; pp. 504–511. doi: 10.1109/BIBM47256.2019.8983134
74.
Schulte-Sasse, R.; Budach, S.; Hnisz, D.; et al. Graph convolutional networks improve the prediction of cancer driver genes. In Proceedings of the 28th International Conference on Artificial Neural Networks, Munich, 17–19 September 2019; Springer: Munich, 2019; pp. 658–668. doi: 10.1007/978-3-030-30493-5_60
75.
Cai, R.C.; Chen, X.X.; Fang, Y.; et al. Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers. Bioinformatics, 2020, 36: 4458−4465.
76.
Chereda, H.; Bleckmann, A.; Menck, K.; et al. Explaining decisions of graph convolutional neural networks: Patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer. Genome Med., 2021, 13: 42.
77.
Rhee, S.; Seo, S.; Kim, S. Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, 13–19 July 2018; IJCAI.org: Stockholm, 2018; pp. 3527–3534.
78.
Pan, X.Y.; Shen, H.B. Inferring disease-associated microRNAs using semi-supervised multi-label graph convolutional networks. iScience, 2019, 20: 265−277.
79.
Li, J.; Zhang, S.; Liu, T.; et al. Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction. Bioinformatics, 2020, 36: 2538−2546.
80.
Li, C.Y.; Liu, H.J.; Hu, Q.; et al. A novel computational model for predicting microRNA-disease associations based on heterogeneous graph convolutional networks. Cells, 2019, 8: 977.
81.
Wang, L.; You, Z.H.; Li, Y.M.; et al. GCNCDA: A new method for predicting circRNA-disease associations based on graph convolutional network algorithm. PLoS Comput. Biol., 2020, 16: e1007568.
82.
Wu, X.M.; Lan, W.; Dong, Y.F.; et al. Inferring lncRNA-disease associations based on graph autoencoder matrix completion. Comput. Biol. Chem., 2020, 87: 107282.
83.
Ding, Y.L.; Tian, L.P.; Lei, X.J.; et al. Variational graph auto-encoders for miRNA-disease association prediction. Methods, 2021, 192: 25−34.
84.
Ding, Y.L.; Lei, X.J.; Liao, B.; et al. Predicting miRNA-disease associations based on multi-view variational graph auto-encoder with matrix factorization. IEEE J. Biomed. Health Inf., 2022, 26: 446−457.
85.
Ding, Y.L.; Lei, X.J.; Liao, B.; et al. MLRDFM: A multi-view laplacian regularized deepFM model for predicting miRNA-disease associations. Brief. Bioinform., 2022, 23: bbac079.
86.
Sun, M.Y.; Zhao, S.D.; Gilvary, C.; et al. Graph convolutional networks for computational drug development and discovery. Brief. Bioinform., 2020, 22: 919−935.
87.
Zamora-Resendiz, R.; Crivelli, S. Structural learning of proteins using graph convolutional neural networks. bioRxiv 2019, in press. doi: 10.1101/610444
88.
Gligorijević, V.; Renfrew, P.D.; Kosciolek, T.; et al. Structure-based protein function prediction using graph convolutional networks. Nat. Commun., 2021, 12: 3168.
89.
Feng, Q.Y.; Dueva, E.; Cherkasov, A.; et al. PADME: A deep learning-based framework for drug-target interaction prediction. arXiv: 1807.09741, 2018. Available online: https://arxiv.org/abs/1807.09741(accessed on 12 October 2022).
90.
Tran, H.N.T.; Thomas, J.J.; Ahamed Hassain Malim, N.H. DeepNC: A framework for drug-target interaction prediction with graph neural networks. PeerJ, 2022, 10: e13163.
91.
Huang, Y.A.; Hu, P.W.; Chan, K.C.C.; et al. Graph convolution for predicting associations between miRNA and drug resistance. Bioinformatics, 2020, 36: 851−858.
92.
Liu, Q.; Hu, Z.Q.; Jiang, R.; et al. DeepCDR: A hybrid graph convolutional network for predicting cancer drug response. Bioinformatics, 2020, 36: i911−i918.
93.
Singha, M.; Pu, L.M.; Shawky, A.E.M.; et al. GraphGR: A graph neural network to predict the effect of pharmacotherapy on the cancer cell growth. bioRxiv 2020, in press. doi: 10.1101/2020.05.20.107458
94.
Zitnik, M.; Agrawal, M.; Leskovec, J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 2018, 34: i457−i466.
95.
Ma, T.F.; Xiao, C.; Zhou, J.Y.; et al. Drug similarity integration through attentive multi-view graph auto-encoders. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, 13 July 2018; AAAI: Stockholm, 2018; pp. 3477–3483. doi: 10.5555/3304222.3304251
96.
Wang, F.; Lei, X.J.; Liao, B.; et al. Predicting drug-drug interactions by graph convolutional network with multi-kernel. Brief. Bioinform., 2022, 23: bbab511.
97.
Liu, L.L.; Heng, J.H.; Quan, Q.; et al. A survey on U-shaped networks in medical image segmentations. Neurocomputing, 2020, 409: 244−258.
98.
Liu, L.L.; Chen, S.W.; Zhang, F.H.; et al. Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI. Neural Comput. Appl., 2020, 32: 6545−6558.
99.
Liu, L.L.; Kurgan, L.; Wu, F.X.; et al. Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Med. Image Anal., 2020, 65: 101791.
100.
Liu, L.L.; Wu, F.X.; Wang, Y.P.; et al. Multi-receptive-field CNN for semantic segmentation of medical images. IEEE J. Biomed. Health Inf., 2020, 24: 3215−3225.
101.
Parisot, S.; Ktena, S.I.; Ferrante, E.; et al. Disease prediction using graph convolutional networks: Application to autism spectrum disorder and Alzheimer’s disease. Med. Image Anal., 2018, 48: 117−130.
102.
Gopinath, K.; Desrosiers, C.; Lombaert, H. Graph convolutions on spectral embeddings for cortical surface parcellation. Med. Image Anal., 2019, 54: 297−305.
103.
Ktena, S.I.; Parisot, S.; Ferrante, E.; et al. Distance metric learning using graph convolutional networks: Application to functional brain networks. In Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, 11–13 September 2017; Springer: Quebec City, 2017; 469–477. doi: 10.1007/978-3-319-66182-7_54
104.
Zhai, Z.W.; Staring, M.; Zhou, X.H.; et al. Linking convolutional neural networks with graph convolutional networks: Application in pulmonary artery-vein separation. In Proceedings of the First International Workshop on Graph Learning in Medical Imaging, Shenzhen, 14 November 2019; Springer: Shenzhen, 2019; pp. 36–43. doi: 10.1007/978-3-030-35817-4_5
105.
Zhang, Y.; Bellec, P. Transferability of brain decoding using graph convolutional networks. bioRxiv 2020, in press. doi: 10.1101/2020.06.21.163964
106.
Yang, H.Z.; Li, X.X.; Wu, Y.F.; et al. Interpretable multimodality embedding of cerebral cortex using attention graph network for identifying bipolar disorder. In Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, 10 October 2019; Springer: Shenzhen, 2019; pp. 799–807. doi: 10.1007/978-3-030-32248-9_89
107.
Chen, B.L.; Shang, X.Q.; Li, M.; et al. Identifying individual-cancer-related genes by rebalancing the training samples. IEEE Trans. Nanobiosci., 2016, 15: 309−315.
108.
Chen, B.L.; Shang, X.Q.; Li, M.; et al. A two-step logistic regression algorithm for identifying individual-cancer-related genes. In Proceedings of 2015 IEEE International Conference on Bioinformatics and Biomedicine, Washington, 09–12 November 2015; IEEE: Washington, 2015; pp. 195–200. doi: 10.1109/BIBM.2015.7359680
109.
Chen, B.L.; Li, M.; Wang, J.X.; et al. A fast and high performance multiple data integration algorithm for identifying human disease genes. BMC Med. Genomics, 2015, 8: S2.
110.
Chen, B.L.; Li, M.; Wang, J.X.; et al. Disease gene identification by using graph kernels and Markov random fields. Sci. China Life Sci., 2014, 57: 1054−1063.
111.
Luo, P.; Tian, L.P.; Ruan, J.S.; et al. Disease gene prediction by integrating PPI networks, clinical RNA-SEQ data and OMIM data. IEEE/ACM Trans. Comput. Biol. Bioinform., 2019, 16: 222−232.
112.
Luo, P.; Tian, L.P.; Ruan, J.S.; et al. Identifying disease genes from PPI networks weighted by gene expression under different conditions. In Proceedings of 2016 IEEE International Conference on Bioinformatics and Biomedicine, Shenzhen, 15-18 December 2016; IEEE: Shenzhen, 2016; pp. 1259–1264. doi: 10.1109/BIBM.2016.7822699
113.
Wang, Y.X.; Zhang, Y.J. Nonnegative matrix factorization: A comprehensive review. IEEE Trans. Knowl. Data Eng., 2013, 25: 1336−1353.
114.
Li, L.X.; Wu, L.; Zhang, H.S.; et al. A fast algorithm for nonnegative matrix factorization and its convergence. IEEE Trans. Neural Netw. Learn. Syst., 2014, 25: 1855−1863.
115.
Shi, Y. Multiclass spectral clustering. In Proceedings of the Ninth IEEE International Conference on Computer Vision, Nice, 13-16 October 2003; IEEE: Nice, 2003; pp. 313–319. doi: 10.1109/ICCV.2003.1238361
116.
Kumar, A.; Rai, P.; Daumé III, H. Co-regularized multi-view spectral clustering. In Proceedings of the 24th International Conference on Neural Information Processing Systems, Granada, 12 December 2011; Curran Associates Inc.: Granada, 2011; pp. 1413–1421. doi: 10.5555/2986459.2986617
117.
Bolla, M. Spectral Clustering and Biclustering; Wiley: Chichester, 2013.
118.
Tiain, L.P.; Luo, P.; Wang, H.Y.; et al. CASNMF: A converged algorithm for symmetrical nonnegative matrix factorization. Neurocomputing, 2018, 275: 2031−2040.
119.
Pascual-Montano, A.; Carmona-Saez, P.; Chagoyen, M.; et al. bioNMF: A versatile tool for non-negative matrix factorization in biology. BMC Bioinformatics, 2006, 7: 366.
120.
Tian, L.P.; Liu, L.Z.; Wu, F.X. Matrix decomposition methods in bioinformatics. Curr. Bioinform, 2013, 8: 259−266.
121.
Fujita, N.; Mizuarai, S.; Murakami, K.; et al. Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses. Sci. Rep., 2018, 8: 9743.
122.
Jamali, A.A.; Kusalik, A.; Wu, F.X. MDIPA: A microRNA-drug interaction prediction approach based on non-negative matrix factorization. Bioinformatics, 2020, 36: 5061−5067.
123.
Jamali, A.A.; Kusalik, A.J.; Wu, F.X. NMTF-DTI: A nonnegative matrix tri-factorization approach with multiple kernel fusion for drug-target interaction prediction. IEEE/ACM Trans. Comput. Biol. Bioinform., 2021, in press. doi: 10.1109/TCBB.2021.3135978
124.
Luo, P.; Xiao, Q.H.; Wei, P.J.; et al. Identifying disease-gene associations with graph-regularized manifold learning. Front. Genet., 2019, 10: 270.
125.
Lin, Y.; Ma, X.K. Predicting lincRNA-disease association in heterogeneous networks using co-regularized non-negative matrix factorization. Front. Genet., 2021, 11: 622234.
126.
Peng, L.; Yang, C.; Huang, L.; et al. RNMFLP: Predicting circRNA-disease associations based on robust nonnegative matrix factorization and label propagation. Brief. Bioinform., 2022, 23: bbac155.
127.
Chen, X.; Li, S.X.; Yin, J.; et al. Potential miRNA-disease association prediction based on kernelized Bayesian matrix factorization. Genomics, 2020, 112: 809−819.
128.
Ding, Y.L.; Wang, F.; Lei, X.J.; et al. Deep belief network-based matrix factorization model for microRNA-disease associations prediction. Evol. Bioinform., 2020, 16: 1176934320919707.
129.
Jamali, A.A.; Tan, Y.T.; Kusalik, A.; et al. NTD-DR: Nonnegative tensor decomposition for drug repositioning. PLoS One, 2022, 17: e0270852.
130.
Wang, X.J.; Gulbahce, N.; Yu, H.Y. Network-based methods for human disease gene prediction. Brief. Funct. Genomics, 2011, 10: 280−293.
131.
Wang, J.X.; Li, M.; Wang, H.; et al. Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Trans. Comput. Biol. Bioinform., 2012, 9: 1070−1080.
132.
Luo, P.; Li, Y.Y.; Tian, L.P.; et al. Enhancing the prediction of disease-gene associations with multimodal deep learning. Bioinformatics, 2019, 35: 3735−3742.
133.
Chen, B.L.; Fan, W.W.; Liu, J.; et al. Identifying protein complexes and functional modules—from static PPI networks to dynamic PPI networks. Brief. Bioinform., 2014, 15: 177−194.
134.
Wang, J.X.; Peng, X.Q.; Peng, W.; et al. Dynamic protein interaction network construction and applications. Proteomics, 2014, 14: 338−352.
135.
Meng, X.M.; Li, M.; Wang, J.X.; et al. Construction of the spatial and temporal active protein interaction network for identifying protein complexes. In Proceedings of 2016 IEEE International Conference on Bioinformatics and Biomedicine, Shenzhen, 15–18 December 2016; IEEE: Shenzhen, 2016; pp. 631–636. doi: 10.1109/BIBM.2016.7822592
136.
Xiao, Q.H.; Wang, J.X.; Peng, X.Q.; et al. Identifying essential proteins from active PPI networks constructed with dynamic gene expression. BMC Genomics, 2015, 16 Suppl 3: S1.
137.
Zhang, W.; Xu, J.; Li, Y.Y.; et al. Detecting essential proteins based on network topology, gene expression data, and gene ontology information. IEEE/ACM Trans. Comput. Biol. Bioinform., 2018, 15: 109−116.
138.
Guo, M.G.; Sosa, D.N.; Altman, R.B. Challenges and opportunities in network-based solutions for biological questions. Brief. Bioinform., 2022, 23: bbab437.
Issue
Volume 2, Issue 1How to Cite
Ding, Y., Fu, M., Luo, P., & Wu, F.-X. (2023). Network Learning for Biomarker Discovery. International Journal of Network Dynamics and Intelligence, 2(1), 51–65. https://doi.org/10.53941/ijndi0201004
RIS
BibTex
Copyright & License
Copyright (c) 2023 by the authors.
This work is licensed under a This work is licensed under a Creative Commons Attribution 4.0 International License.