Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;3:673.

Article
CAS
PubMed
Google Scholar

Booth B, Zemmel R. Prospects for productivity. Nat Rev Drug Discov. 2004;3:451.

Article
CAS
PubMed
Google Scholar

Dudley JT, Deshpande T, Butte AJ. Exploiting drug–disease relationships for computational drug repositioning. Brief Bioinform. 2011;12(4):303–11.

Article
CAS
PubMed
PubMed Central
Google Scholar

Nagaraj AB, Wang QQ, Joseph P, Zheng C, Chen Y, Kovalenko O, Singh S, Armstrong A, Resnick K, Zanotti K. Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment. Oncogene. 2018;37(3):403–14.

Article
CAS
PubMed
Google Scholar

Luo H, Wang J, Li M, Luo J, Peng X, Wu FX, Pan Y. Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics. 2016;32(17):2664.

Article
CAS
PubMed
Google Scholar

Luo H, Li M, Wang S, Liu Q, Li Y, Wang J. Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics. 2018;34(11):1904–12.

Article
CAS
PubMed
Google Scholar

Chen X, Sun Y-Z, Zhang D-H, Li J-Q, Yan G-Y, An J-Y, You Z-H: NRDTD: a database for clinically or experimentally supported non-coding RNAs and drug targets associations. Database. 2017;2017:bax057.

Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2009;38(suppl_1):D355–60.

Article
PubMed
PubMed Central
CAS
Google Scholar

Hamosh A, Scott AF, Amberger J, Bocchini C, Valle D, Mckusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33(1):514–7.

Google Scholar

Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet J-P, Subramanian A, Ross KN. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795):1929–35.

Article
CAS
PubMed
Google Scholar

Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V. DrugBank 30: a comprehensive resource for ‘Omics’ research on drugs. Nucleic Acids Res. 2011;39(Database issue):D1035.

Article
CAS
PubMed
Google Scholar

Kuhn M, Szklarczyk D, Pletscher-Frankild S, Blicher TH, Von MC, Jensen LJ, Bork P. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res. 2014;42(Database issue):401–7.

Article
CAS
Google Scholar

Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, Mcglinchey S, Michalovich D, Al-Lazikani B. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40(Database issue):1100–7.

Article
CAS
Google Scholar

Meng F-R, You Z-H, Chen X, Zhou Y, An J-Y. Prediction of drug–target interaction networks from the integration of protein sequences and drug chemical structures. Molecules. 2017;22(7):1119.

Article
PubMed Central
CAS
Google Scholar

Luo H, Chen J, Shi L, Mikailov M, Zhu H, Wang K, He L, Yang L. DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome. Nucleic Acids Res. 2011;39(suppl_2):W492–8.

Article
CAS
PubMed
PubMed Central
Google Scholar

Guo Z-H, You Z-H, Huang D-S, Yi H-C, Chen Z-H, Wang Y-B. A learning based framework for diverse biomolecule relationship prediction in molecular association network. Commun Biol. 2020;3(1):118.

Article
PubMed
PubMed Central
Google Scholar

Yi H-C, You Z-H, Huang D-S, Li X, Jiang T-H, Li L-P. A deep learning framework for robust and accurate prediction of ncRNA-protein interactions using evolutionary information. Mol Ther Nucleic Acids. 2018;11:337–44.

Article
CAS
PubMed
PubMed Central
Google Scholar

Yi H-C, You Z-H, Cheng L, Zhou X, Jiang T-H, Li X, Wang Y-B. Learning distributed representations of RNA and protein sequences and its application for predicting lncRNA-protein interactions. Comput Struct Biotechnol J. 2020;18:20–6.

Article
CAS
PubMed
Google Scholar

He T, Bai L, Ong Y. Manifold regularized stochastic block model. In: 2019 IEEE 31st international conference on tools with artificial intelligence (ICTAI). 2019. P. 800–7.

He T, Chan KCC. Discovering fuzzy structural patterns for graph analytics. IEEE Trans Fuzzy Syst. 2018;26(5):2785–96.

Article
Google Scholar

He T, Chan KCC. MISAGA: an algorithm for mining interesting subgraphs in attributed graphs. IEEE Trans Cybern. 2018;48(5):1369–82.

Article
PubMed
Google Scholar

He T, Chan KCC. Measuring boundedness for protein complex identification in PPI networks. IEEE/ACM Trans Comput Biol Bioinf. 2019;16(3):967–79.

Article
CAS
Google Scholar

He T, Liu Y, Ko TH, Chan KCC, Ong YS. Contextual correlation preserving multiview featured graph clustering. IEEE Trans Cybern. 2020;50(10):4318–4331.

Yi H-C, You Z-H, Huang D-S, Guo Z-H, Chan KC, Li Y. Learning representations to predict intermolecular interactions on large-scale heterogeneous molecular association network. iScience. 2020;23(7):101261.

Article
CAS
PubMed
PubMed Central
Google Scholar

Yi H-C, You Z-H, Guo Z-H. Construction and analysis of molecular association network by combining behavior representation and node attributes. Front Genet. 2019;10:1106.

Article
CAS
PubMed
PubMed Central
Google Scholar

Chiang AP, Butte AJ. Systematic evaluation of drug–disease relationships to identify leads for novel drug uses. Clin Pharmacol Ther. 2009;86(5):507–10.

Article
CAS
PubMed
Google Scholar

Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol. 2011;7(1):496.

Article
PubMed
PubMed Central
CAS
Google Scholar

Francesco N, Yan Z, Moreira VM, Roberto T, Juha K, Mauro DA, Dario G. Drug repositioning: a machine-learning approach through data integration. J Cheminform. 2013;5(1):30–30.

Article
CAS
Google Scholar

Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R, Brunetti-Pierri N, Isacchi A. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci. 2010;107(33):14621–6.

Article
CAS
PubMed
PubMed Central
Google Scholar

Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, Zhou W, Huang J, Tang Y. Prediction of drug–target interactions and drug repositioning via network-based inference. PLoS Comput Biol. 2012;8(5):e1002503.

Article
CAS
PubMed
PubMed Central
Google Scholar

Wu C, Gudivada RC, Aronow BJ, Jegga AG. Computational drug repositioning through heterogeneous network clustering. BMC Syst Biol. 2013;7(5):1–9.

Google Scholar

Wang W, Yang S, Zhang X, Li J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics. 2014;30(20):2923–30.

Article
CAS
PubMed
PubMed Central
Google Scholar

Martínez V, Navarro C, Cano C, Fajardo W, Blanco A. DrugNet: Network-based drug–disease prioritization by integrating heterogeneous data. Artif Intell Med. 2015;63(1):41–9.

Article
PubMed
Google Scholar

Zeng X, Zhu S, Liu X, Zhou Y, Nussinov R, Cheng F. deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics. 2019;35(24):5191–8.

Article
CAS
PubMed
PubMed Central
Google Scholar

Chen H, Cheng F, Li J. iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding. PLoS Comput Biol. 2020;16(7):e1008040.

Article
CAS
PubMed
PubMed Central
Google Scholar

Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008;36(Database issue):901–6.

Article
CAS
Google Scholar

Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E. The Chemistry Development Kit (CDK): an open-source Java library for chemo-and bioinformatics. J Chem Inf Comput Sci. 2003;43(2):493–500.

Article
CAS
PubMed
PubMed Central
Google Scholar

Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci. 1988;28(1):31–6.

Article
CAS
Google Scholar

Vanunu O, Magger O, Ruppin E, Shlomi T, Sharan R. Associating genes and protein complexes with disease via network propagation. PLoS Comput Biol. 2010;6(1):e1000641.

Article
PubMed
PubMed Central
CAS
Google Scholar

Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein–protein interaction networks. Nat Methods. 2012;9(5):471.

Article
CAS
PubMed
PubMed Central
Google Scholar

Yu L, Huang J, Ma Z, Zhang J, Zou Y, Gao L. Inferring drug-disease associations based on known protein complexes. BMC Med Genomics. 2015;8(2):S2.

Article
PubMed
PubMed Central
Google Scholar

van Laarhoven T, Nabuurs SB, Marchiori E. Gaussian interaction profile kernels for predicting drug–target interaction. Bioinformatics. 2011;27(21):3036–43.

Article
PubMed
CAS
Google Scholar

Chen X, Jiang Z-C, Xie D, Huang D-S, Zhao Q, Yan G-Y, You Z-H. A novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction. Mol BioSyst. 2017;13(6):1202–12.

Article
CAS
PubMed
Google Scholar

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.

Article
CAS
PubMed
Google Scholar

Gers FA, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. 1999.

Shen Z, Bao W, Huang D-S. Recurrent neural network for predicting transcription factor binding sites. Sci Rep. 2018;8(1):15270.

Article
PubMed
PubMed Central
CAS
Google Scholar

Yi H-C, You Z-H, Zhou X, Cheng L, Li X, Jiang T-H, Chen Z-H. ACP-DL: a deep learning long short-term memory model to predict anticancer peptides using high-efficiency feature representation. Mol Ther Nucleic Acids. 2019;17:1–9.

Article
CAS
PubMed
PubMed Central
Google Scholar

Wang Y-B, You Z-H, Yang S, Yi H-C, Chen Z-H, Zheng K. A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network. BMC Med Inform Decis Mak. 2020;20(2):49.

Article
PubMed
PubMed Central
Google Scholar

Cho K, Van Merriënboer B, Bahdanau D, Bengio Y: On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:14091259. 2014.

Chung J, Gulcehre C, Cho K, Bengio Y: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:14123555. 2014.

Chollet F. Keras: The python deep learning library. Astrophysics Source Code Library. 2018.

Gal Y, Hron J, Kendall A. Concrete dropout. 2017. arXiv preprint arXiv:1705.07832.

Kingma DP, Ba J. Adam: a method for stochastic optimization. 2014. arXiv preprint arXiv:1412.6980v3.

Yi H-C, You Z-H, Guo Z-H, Huang D-S, Chan KCC. Learning representation of molecules in association network for predicting intermolecular associations. IEEE/ACM Trans Comput Biol Bioinform. 2020. https://doi.org/10.1109/TCBB.2020.2973091.

Yi H-C, You Z-H, Wang M-N, Guo Z-H, Wang Y-B, Zhou J-R. RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information. BMC Bioinform. 2020;21(1):60.

Article
CAS
Google Scholar