Scilight Press

Author Information

Received: 14 Oct 2022 | Accepted: 05 Dec 2022 | Published: 27 Mar 2023

Abstract

Graphical Abstract

Image Viewer

Open in new tab

Download Image

Keywords

network learning | graph | centrality | graph convolutional network | biomarker discovery

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.