1.Xia Z, Wu L-Y, Zhou X, Wong STC: Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. Bmc Systems Biology 2010, 4.
2.Wang Y-C, Yang Z-X, Wang Y, Deng N-Y: Computationally Probing Drug-Protein Interactions Via Support Vector Machine. Letters in Drug Design & Discovery 2010, 7(5):370–378.
3.Landry Y, Gies J-P: Drugs and their molecular targets: an updated overview. Fundamental & Clinical Pharmacology 2008, 22(1):1–18.
4.Li Q, Lai L: Prediction of potential drug targets based on simple sequence properties. Bmc Bioinformatics 2007, 8.
5.van de Waterbeemd H, Gifford E: ADMET in silico modelling: Towards prediction paradise? Nature Reviews Drug Discovery 2003, 2(3):192–204.
6.Kuruvilla FG, Shamji AF, Sternson SM, Hergenrother PJ, Schreiber SL: Dissecting glucose signalling with diversity-oriented synthesis and small-molecule microarrays. Nature 2002, 416(6881):653–657.
7.Haggarty SJ, Koeller KM, Wong JC, Butcher RA, Schreiber SL: Multidimensional chemical genetic analysis of diversity-oriented synthesis-derived deacetylase inhibitors using cell-based assays. Chemistry & Biology 2003, 10(5):383–396.
8.Wang L, You ZH, Chen X, Li JQ, Yan X, Zhang W, Huang YA: An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences. Oncotarget 2017, 8(3):5149.
9.Chen X, Yan CC, Zhang X, Zhang X, Dai F, Yin J, Zhang Y: Drug-target interaction prediction: databases, web servers and computational models. Briefings in Bioinformatics 2016, 17(4):696.
10.Wu Z, Cheng F, Li J, Li W, Liu G, Tang Y: SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug–target interactions and drug repositioning. Briefings in Bioinformatics 2017, 18(2):333–347.
11.Zhang W, Chen Y, Li D: Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information. Molecules 2017, 22(12):2056.
12.Zong N, Kim H, Ngo V, Harismendy O: Deep Mining Heterogeneous Networks of Biomedical Linked Data to Predict Novel Drug-Target Associations. Bioinformatics 2017, 33(15).
13.Peng L, Liao B, Zhu W, Li Z, Li K: Predicting Drug-Target Interactions With Multi-Information Fusion. IEEE Journal of Biomedical & Health Informatics 2017, 21(2):561–572.
14.Ezzat A, Wu M, Li XL, Kwoh CK: Drug-Target Interaction Prediction using Ensemble Learning and Dimensionality Reduction. Methods 2017, 129:81.
15.Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, Lu H: Deep-Learning-Based Drug-Target Interaction Prediction. Journal of Proteome Research 2017, 16(4):1401.
16.Zweig MH, Campbell G: Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical chemistry 1993, 39(4):561–577.
17.Wang L, Wang H-F, Liu S-R, Yan X, Song K-J: Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest. Scientific reports 2019, 9(1):9848.
18.Wang H, Song A, Li B, Xu B, Li Y: Psychophysiological classification and experiment study for spontaneous EEG based on two novel mental tasks. Technology and Health Care 2015, 23:S249-S262.
19.Li Y, Olson EB: A General Purpose Feature Extractor for Light Detection and Ranging Data. Sensors 2010, 10(11):10356–10375.
20.Li Y, Olson EB, Ieee: Structure Tensors for General Purpose LIDAR Feature Extraction. In: IEEE International Conference on Robotics and Automation (ICRA): 2011
May 09–13 2011; Shanghai, PEOPLES R CHINA. 2011: 1869–1874.
21.Ojansivu V, Heikkila J: Blur insensitive texture classification using local phase quantization. Image and Signal Processing 2008, 5099:236–243.
22.Gonen M: Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics 2012, 28(18):2304–2310.
23.Chen H, Zhang Z: A Semi-Supervised Method for Drug-Target Interaction Prediction with Consistency in Networks. Plos One 2013, 8(5).
24.Öztürk H, Ozkirimli E, Özgür A: A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction. BMC Bioinformatics 2016, 17(1):1–11.
25.Wang L, You ZH, Chen X, Yan X, Liu G, Zhang W: RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information. Current Protein & Peptide Science 2018, 19(5):445–454.
26.Gunther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ et al: SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Research 2008, 36:D919-D922.
27.Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M: Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 2008, 24(13):I232-I240.
28.Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G, Schomburg D: BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Research 2004, 32:D431-D433.
29.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 Research 2008, 36:D901-D906.
30.Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Research 2006, 34:D354-D357.
31.Li X, Zhu M, Brasier AR, Kudlicki AS: Inferring Genome-Wide Functional Modulatory Network: A Case Study on NF-kappa B/RelA Transcription Factor. Journal of Computational Biology 2015, 22(4):300–312.
32.Li XL, Zhao YX, Tian B, Jamaluddin M, Mitra A, Yang J, Rowicka M, Brasier AR, Kudlicki A: Modulation of Gene Expression Regulated by the Transcription Factor NF-kappa B/RelA. Journal of Biological Chemistry 2014, 289(17):11927–11944.
33.Yang J, Zhao Y, Kalita M, Li X, Jamaluddin M, Tian B, Edeh CB, Wiktorowicz JE, Kudlicki A, Brasier AR: Systematic Determination of Human Cyclin Dependent Kinase (CDK)–9 Interactome Identifies Novel Functions in RNA Splicing Mediated by the DEAD Box (DDX)–5/17 RNA Helicases. Molecular & Cellular Proteomics 2015, 14(10):2701–2721.
34.Gribskov M, McLachlan AD, Eisenberg D: Profile analysis: detection of distantly related proteins. Proceedings of the National Academy of Sciences of the United States of America 1987, 84(13):4355–4358.
35.Chen X-W, Jeong JC: Sequence-based prediction of protein interaction sites with an integrative method. Bioinformatics 2009, 25(5):585–591.
36.Wang L, You ZH, Chen X, Xia SX, Liu F, Yan X, Zhou Y, Song KJ: A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network. Journal Of Computational Biology 2018, 25(3):361–373.
37.Jones DT: Protein secondary structure prediction based on position-specific scoring matrices. Journal of molecular biology 1999, 292(2):195–202.
38.Jones DT, Ward JJ: Prediction of disordered regions in proteins from position specific score matrices. Proteins-Structure Function and Bioinformatics 2003, 53:573–578.
39.Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research 1997, 25(17):3389–3402.
40.Wang L, You Z-H, Xia S-X, Liu F, Chen X, Yan X, Zhou Y: Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier. Journal Of Theoretical Biology 2017, 418:105–110.
41.Chou KC: Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins-Structure Function and Genetics 2001, 43(3):246–255.
42.Rodriguez JJ, Kuncheva LI: Rotation forest: A new classifier ensemble method. Ieee Transactions on Pattern Analysis and Machine Intelligence 2006, 28(10):1619–1630.
43.Wang L, You Z-H, Yan X, Xia S-X, Liu F, Li L-P, Zhang W, Zhou Y: Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions. Scientific reports 2018, 8(1):12874.