[1]
Keller, T.H.; Pichota, A.; Yin, Z. A practical view of druggability. Curr. Opin. Chem. Biol., 2006, 10, 357-361.
[2]
Bakheet, T.M.; Doig, A.J. Properties and identification of human protein drug targets. Bioinformatics, 2009, 25, 451-457.
[3]
Hopkins, A.L.; Groom, C.R. The druggable genome. Nat. Rev. Drug Discov., 2002, 1, 727-730.
[4]
Drews, J. Drug discovery: a historical perspective. Science, 2000, 287, 1960-1964.
[5]
Li, Z.C.; Zhong, W.Q.; Liu, Z.Q.; Huang, M.H.; Xie, Y.; Dai, Z.; Zou, X.Y. Large-scale identification of potential drug targets based on the topological features of human protein-protein interaction network. Anal. Chim. Acta, 2015, 871, 18-27.
[6]
Overington, J.P.; Allazikani, B.; Hopkins, A.L. How many drug targets are there? Nat. Rev. Drug Discov., 2006, 5, 993-996.
[7]
Zhu, M.; Gao, L.; Li, X.; Liu, Z.; Xu, C.; Yan, Y.; Walker, E.; Jiang, W.; Su, B.; Chen, X. The analysis of the drug-targets based on the topological properties in the human protein-protein interaction network. J. Drug Target., 2009, 17, 524-532.
[8]
Xiao, X.; Wang, P.; Chou, K.C. GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes. J. Comput. Chem., 2010, 30, 1414-1423.
[9]
Zheng, C.J.; Han, L.Y.; Yap, C.W.; Ji, Z.L.; Cao, Z.W.; Chen, Y.Z. Therapeutic targets: Progress of their exploration and investigation of their characteristics. Pharmacol. Rev., 2006, 58, 259.
[10]
Feng, P.; Hui, D.; Hao, L.; Wei, C. AOD: The antioxidant protein database. Sci. Rep., 2017, 7, 7449.
[11]
Zhang, T.; Tan, P.; Wang, L.; Jin, N.; Wang, D. RNALocate: A resource for RNA subcellular localizations. Nucleic Acids Res., 2016, 45, D135-D138.
[12]
Liang, Z.Y.; Lai, H.Y.; Yang, H.; Zhang, C.J.; Yang, H.; Wei, H.H.; Chen, X.X.; Zhao, Y.W.; Su, Z.D.; Li, W.C. Pro54DB: a database for experimentally verified sigma-54 promoters. Bioinformatics, 2017, 33, 467.
[13]
Law, V.; Knox, C.; Djoumbou, Y.; Jewison, T.; Guo, A.C.; Liu, Y.; Maciejewski, A.; Arndt, D.; Wilson, M.; Neveu, V. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res., 2014, 42, D1091.
[14]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res., 2002, 28, 235.
[15]
Hewett, M.; Oliver, D.E.; Rubin, D.L.; Easton, K.L.; Stuart, J.M.; Altman, R.B.; Klein, T.E. PharmGKB: The Pharmacogenetics Knowledge Base. Nucleic Acids Res., 2002, 30, 163.
[16]
Peter, D.A.; Grondin, M.C.; Robin, J.; Lay, J.M.; Kelley, L.H.; Cynthia, S.R.; Daniela, S.; King, B.L.; Rosenstein, M.C.; Wiegers, T.C. The comparative toxicogenomics database: Update 2013. Nucleic Acids Res., 2013, 41, D1104-D1114.
[17]
Lim, E.; Pon, A.; Djoumbou, Y.; Knox, C.; Shrivastava, S.; Guo, A.C.; Neveu, V.; Wishart, D.S. T3DB: A comprehensively annotated database of common toxins and their targets. Nucleic Acids Res., 2010, 38, D781-D786.
[18]
Pontn, F.; Jirstrm, K.; Uhlen, M. The human protein atlas--a tool for pathology. J. Pathol., 2010, 216, 387-393.
[19]
Zhu, F.; Shi, Z.; Qin, C.; Tao, L.; Liu, X.; Xu, F.; Zhang, L.; Song, Y.; Liu, X.; Zhang, J. Therapeutic target database update 2012: A resource for facilitating target-oriented drug discovery. Nucleic Acids Res., 2012, 40, D1128.
[20]
Gao, Z.; Li, H.; Zhang, H.; Liu, X.; Kang, L.; Luo, X.; Zhu, W.; Chen, K.; Wang, X.; Jiang, H. PDTD: A web-accessible protein database for drug target identification. BMC Bioinformatics, 2008, 9, 104.
[21]
Stuart, A.C.; Ilyin, V.A.; Sali, A. LigBase: a database of families of aligned ligand binding sites in known protein sequences and structures. Bioinformatics, 2002, 18, 200-201.
[22]
Ivanisenko, V.A.; Pintus, S.S.; Grigorovich, D.A.; Kolchanov, N.A. PDBSite: A database of the 3D structure of protein functional sites. Nucleic Acids Res., 2005, 33, D183.
[23]
Gold, N.D.; Jackson, R.M. SitesBase: A database for structure-based protein-ligand binding site comparisons. Nucleic Acids Res., 2006, 34, 231-234.
[24]
Golovin, A.; Dimitropoulos, D.; Oldfield, T.; Rachedi, A.; Henrick, K. MSDsite: A database search and retrieval system for the analysis and viewing of bound ligands and active sites. Proteins, 2005, 58, 190-199.
[25]
Peter, B. A, S.C.; Ingo, D.; Gerhard, K. AffinDB: a freely accessible database of affinities for protein-ligand complexes from the PDB. Nucleic Acids Res., 2006, 34, 522-526.
[26]
Dolado, I.; Swat, A.; Ajenjo, N.; Vita, G.D.; Cuadrado, A.; Nebreda, A.R. p38 MAP kinase as a sensor of reactive oxygen species in tumorigenesis. Cancer Cell, 2007, 11, 191-205.
[27]
Ceruti, S.; Villa, G.; Genovese, T.; Mazzon, E.; Longhi, R.; Rosa, P.; Bramanti, P.; Cuzzocrea, S.; Abbracchio, M.P. The P2Y-like receptor GPR17 as a sensor of damage and a new potential target in spinal cord injury. Brain. A J. Neurol., 2009, 132, 2206.
[28]
Kachel, P.; Trojanowicz, B.; Sekulla, C.; Prenzel, H.; Dralle, H.; Hoangvu, C. Phosphorylation of pyruvate kinase M2 and lactate dehydrogenase A by fibroblast growth factor receptor 1 in benign and malignant thyroid tissue. BMC Cancer, 2015, 15, 1-13.
[29]
Dogrul, A.; Gardell, L.R.; Ossipov, M.H.; Tulunay, F.C.; Lai, J.; Porreca, F. Reversal of experimental neuropathic pain by T-type calcium channel blockers. Pain, 2003, 105, 159-168.
[30]
Pisani, A.; Gubellini, P.; Bonsi, P.; Conquet, F.; Picconi, B.; Centonze, D.; Bernardi, G.; Calabresi, P. Metabotropic glutamate receptor 5 mediates the potentiation of N-methyl-D-aspartate responses in medium spiny striatal neurons. Neuroscience, 2001, 106, 579-587.
[31]
Xue, L.; Gyles, S.L.; Wettey, F.R.; Gazi, L.; Townsend, E.; Hunter, M.G.; Pettipher, R. Prostaglandin D2 causes preferential induction of proinflammatory Th2 cytokine production through an action on chemoattractant receptor-like molecule expressed on Th2 cells. J. Immunol., 2005, 175, 6531.
[32]
Molkentin, J.D.; Lu, J.R.; Antos, C.L.; Markham, B.; Richardson, J.; Robbins, J.; Grant, S.R.; Olson, E.N. A calcineurin-dependent transcriptional pathway for cardiac hypertrophy. Cell, 1998, 93, 215-228.
[33]
Qian, K.C.; Studts, A.J.; Wang, B.L.; Barringer, B.K.; Kronkaitis, B.A.; Peng, B.C.; Baptiste, B.A.; Lafrance, B.R.; Mische, B.S. A, B.F. Expression, purification, crystallization and preliminary crystallographic analysis of human Pim-1 kinase. Acta Crystallogr., 2010, 61, 96-99.
[34]
Hirono, Y.; Yoshimoto, T.; Suzuki, N.; Sugiyama, T.; Sakurada, M.; Takai, S.; Kobayashi, N.; Shichiri, M.; Hirata, Y. Angiotensin II receptor type 1-mediated vascular oxidative stress and proinflammatory gene expression in aldosterone-induced hypertension: the possible role of local renin-angiotensin system. Endocrinology, 2007, 148, 1688-1696.
[35]
Courtney, K.D.; Corcoran, R.B.; Engelman, J.A. The PI3K pathway as drug target in human cancer. J. Clin. Oncol., 2010, 28, 1075.
[36]
Marton, M.J.; Derisi, J.L.; Bennett, H.A.; Iyer, V.R.; Meyer, M.R.; Roberts, C.J.; Stoughton, R.; Burchard, J.; Slade, D.; Dai, H. Drug target validation and identification of secondary drug target effects using DNA microarrays. Tanpakushitsu Kakusan Koso Protein Nucleic Acid Enzyme, 2007, 52, 1808-1809.
[37]
Zhang, Y.L.; Shen, W.P.; Xie, Z.; Wang, L. Adenosine monophosphate affects competence development and plasmid DNA: Transformation in Escherichia coli. Curr. Microbiol., 2013, 67, 550-556.
[38]
Mueller, B.K.; Mack, H.; Teusch, N. Rho kinase, a promising drug target for neurological disorders. Nat. Rev. Drug Discov., 2005, 4, 387-398.
[39]
Chan, D.C.; Chutkowski, C.T.; Kim, P.S. Evidence that a prominent cavity in the coiled coil of HIV type 1 gp41 is an attractive drug target. PNAS, 1998, 95, 15613-15617.
[40]
Zhang, Y.N.; Zhang, W.; Hong, D.; Shi, L.; Shen, Q.; Li, J.Y.; Li, J.; Hu, L.H. Oleanolic acid and its derivatives: New inhibitor of protein tyrosine phosphatase 1B with cellular activities. Bioorg. Med. Chem., 2008, 16, 8697-8705.
[41]
Binda, C.; Newtonvinson, P.; Hubálek, F.; Edmondson, D.E.; Mattevi, A. Structure of human monoamine oxidase B, a drug target for the treatment of neurological disorders. Nat. Struct. Biol., 2001, 9, 22-26.
[42]
Bhat, R.V.; Budd Haeberlein, S.L.; Avila, J. Glycogen synthase kinase 3: A drug target for CNS therapies. J. Neurochem., 2010, 89, 1313-1317.
[43]
Hopkins, A.L. Drug discovery: Predicting promiscuity. Nature, 2009, 462, 167-8.
[44]
Hu, Y.; Zhou, M.; Shi, H.; Ju, H.; Jiang, Q.; Cheng, L. Measuring disease similarity and predicting disease-related ncRNAs by a novel method. BMC Med. Genomics, 2017, 10, 71.
[45]
Cheng, L.; Jiang, Y.; Wang, Z.; Shi, H.; Sun, J.; Yang, H.; Zhang, S.; Hu, Y.; Zhou, M. DisSim: an online system for exploring significant similar diseases and exhibiting potential therapeutic drugs. Sci. Rep., 2016, 6, 30024.
[46]
Cheng, L.; Sun, J.; Xu, W.; Dong, L.; Hu, Y.; Zhou, M. OAHG: An integrated resource for annotating human genes with multi-level ontologies. Sci. Rep., 2016, 10, 34820.
[47]
Jiang, Q.; Jin, S.; Jiang, Y.; Liao, M.; Feng, R.; Zhang, L.; Liu, G.; Hao, J. Alzheimers disease variants with the genome-wide significance are significantly enriched in immune pathways and active in immune cells. Mol. Neurobiol., 2017, 54, 594-600.
[48]
Liu, G.; Zhang, F.; Hu, Y.; Jiang, Y.; Gong, Z.; Liu, S.; Chen, X.; Jiang, Q.; Hao, J. Genetic variants and multiple sclerosis risk gene slc9a9 expression in distinct human brain regions. Mol. Neurobiol., 2017, 54, 6820-6826.
[49]
Hu, Y.; Zheng, L.; Cheng, L.; Zhang, Y.; Bai, W.; Zhou, W.; Wang, T.; Han, Z.; Zong, J.; Jin, S.; Zhang, J.; Liu, G.; Jiang, Q. GAB2 rs2373115 variant contributes to Alzheimers disease risk specifically in European population. J. Neurol. Sci., 2017, 375, 18-22.
[50]
Hu, Y.; Cheng, L.; Zhang, Y.; Bai, W.; Zhou, W.; Wang, T.; Han, Z.; Zong, J.; Jin, S.; Zhang, J.; Jiang, Q.; Liu, G. Rs4878104 contributes to Alzheimers disease risk and regulates DAPK1 gene expression. Neurol. Sci., 2017, 38, 1255-1262.
[51]
Peng, J.; Wang, H.; Lu, J.; Hui, W.; Wang, Y.; Shang, X. Identifying term relations cross different gene ontology categories. BMC Bioinformatics, 2017, 18, 573.
[52]
Peng, J.; Wang, T.; Wang, J.; Wang, Y.; Chen, J. Extending gene ontology with gene association networks. Bioinformatics, 2016, 32, 1185-94.
[53]
Peng, J.J.; Xue, H.S.; Shao, Y.K.; Shang, X.Q.; Wang, Y.D.; Chen, J. A novel method to measure the semantic similarity of HPO terms. Int. J. Data Min. Bioinform., 2017, 17, 173-188.
[54]
Liu, G.; Xu, Y.; Jiang, Y.; Zhang, L.; Feng, R.; Jiang, Q. PICALM rs3851179 variant confers susceptibility to Alzheimers disease in Chinese population. Mol. Neurobiol., 2017, 54, 3131-3136.
[55]
Liu, G.; Zhang, F.; Hu, Y.; Jiang, Y.; Gong, Z.; Liu, S.; Chen, X.; Jiang, Q.; Hao, J. Multiple sclerosis risk pathways differ in Caucasian and Chinese populations. J. Neuroimmunol., 2017, 307, 63-68.
[56]
Liu, G.; Zhang, F.; Jiang, Y.; Hu, Y.; Gong, Z.; Liu, S.; Chen, X.; Jiang, Q.; Hao, J. Integrating genome-wide association studies and gene expression data highlights dysregulated multiple sclerosis risk pathways. Mult. Scler., 2017, 23, 205-212.
[57]
Liu, G.; Zhang, Y.; Wang, L.; Xu, J.; Chen, X.; Bao, Y.; Hu, Y.; Jin, S.; Tian, R.; Bai, W.; Zhou, W.; Wang, T.; Han, Z.; Zong, J.; Jiang, Q. Alzheimers disease rs11767557 variant regulates EPHA1 gene expression specifically in human whole blood. J. Alzheimers Dis., 2017, 61.
[58]
Brehme, M.; Hantschel, O.; Colinge, J.; Kaupe, I.; Planyavsky, M.; Kcher, T.; Mechtler, K.; Bennett, K.L.; Supertifurga, G. Charting the molecular network of the drug target Bcr-Abl. PNAS, 2009, 106, 7414-7419.
[59]
Via, D.; Uriarte, E.; Orallo, F.; González-DÃaz, H. Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. Mol. Pharm., 2009, 6, 825.
[60]
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, e1002503.
[61]
Csermely, P.; Agoston, V.; Pongor, S. The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol. Sci., 2005, 26, 178-182.
[62]
Huang, C.; Zhang, R.; Chen, Z.; Jiang, Y.; Shang, Z.; Sun, P.; Zhang, X.; Li, X. Predict potential drug targets from the ion channel proteins based on SVM. J. Theor. Biol., 2010, 262, 750-756.
[63]
Han, L.Y.; Zheng, C.J.; Xie, B.; Jia, J.; Ma, X.H.; Zhu, F.; Lin, H.H.; Chen, X.; Chen, Y.Z. Support vector machines approach for predicting druggable proteins: Recent progress in its exploration and investigation of its usefulness. Drug Discov. Today, 2007, 12, 304-313.
[64]
Li, Q.; Lai, L. Prediction of potential drug targets based on simple sequence properties. BMC Bioinformatics, 2007, 8, 353.
[65]
Zhao, Y.W.; Su, Z.D.; Yang, W.; Lin, H.; Chen, W.; Tang, H. IonchanPred 2.0: A tool to predict ion channels and their types. Int. J. Mol. Sci., 2017, 18, 1838.
[66]
Lin, H.; Ding, H. Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J. Theor. Biol., 2011, 269, 64.
[67]
Chen, W.; Lin, H. Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine. Comput. Biol. Med., 2012, 42, 504.
[68]
Chen, X.X.; Hua, T.; Li, W.C.; Hao, W.; Wei, C.; Hui, D.; Hao, L. Identification of bacterial cell wall lyases via pseudo amino acid composition. BioMed Res. Int., 2016, 2016, 1-8.
[69]
Yang, H.; Hua, T.; Chen, X.X.; Zhang, C.J.; Zhu, P.P.; Hui, D.; Wei, C.; Hao, L. Identification of secretory proteins in Mycobacterium tuberculosis using pseudo amino acid composition. BioMed Res. Int., 2016, 2016, 5413903.
[70]
Lai, H.Y.; Chen, X.X.; Chen, W.; Tang, H.; Lin, H. Sequence-based predictive modeling to identify cancerlectins. Oncotarget, 2017, 8, 28169-28175.
[71]
Ashrafi, E.; Alemzadeh, A.; Ebrahimi, M.; Ebrahimie, E.; Dadkhodaei, N.; Ebrahimi, M. Amino acid features of P1B-ATPase heavy metal transporters enabling small numbers of organisms to cope with heavy metal pollution. Bioinform. Biol. Insights, 2011, 2011, 59-82.
[72]
Ebrahimi, M.; Ebrahimie, E.; Shamabadi, N.; Ebrahimi, M. Are there any differences between features of proteins expressed in malignant and benign breast cancers? J. Res. Med. Sci., 2010, 15, 299-309.
[73]
Ebrahimi, M.; Lakizadeh, A.; Agha-Golzadeh, P.; Ebrahimie, E.; Ebrahimi, M. Prediction of thermostability from amino acid attributes by combination of clustering with attribute weighting: A new vista in engineering enzymes. PLoS One, 2011, 6, e23146.
[74]
Tahrokh, E.; Ebrahimi, M.; Ebrahimi, M.; Zamansani, F.; Sarvestani, N.R.; Mohammadi-Dehcheshmeh, M.; Ghaemi, M.R.; Ebrahimie, E. Comparative study of ammonium transporters in different organisms by study of a large number of structural protein features via data mining algorithms. Genes Genomics, 2011, 33, 565.
[75]
Zinati, Z.; Zamansani, F.; Kayvanjoo, A.H.; Ebrahimi, M.; Ebrahimi, M.; Ebrahimie, E.; Dehcheshmeh, M.M. New layers in understanding and predicting α-linolenic acid content in plants using amino acid characteristics of omega-3 fatty acid desaturase. Comput. Biol. Med., 2014, 54, 14-23.
[76]
Bakhtiarizadeh, M.R.; Moradi-Shahrbabak, M.; Ebrahimi, M.; Ebrahimie, E. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology. J. Theor. Biol., 2014, 356, 213-222.
[77]
Delavari, A.; Zare, S.; Ghaemi, M.R.; Kashfi, R.; Ebrahimi, M.; Tahmasebi, A.; Ebrahimi, M.; Ebrahimie, E. Determining the structural amino acid attributes which are important in both protein thermostability and alkalophilicity: A case study on xylanase. Biotechnologia, 2014, 2, 161-173.
[78]
Kayvanjoo, A.H.; Ebrahimi, M.; Haqshenas, G. Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms. BMC Res. Notes, 2014, 7, 565.
[79]
Zhao, Y.W.; Lai, H.Y.; Hua, T.; Wei, C.; Hao, L. Prediction of phosphothreonine sites in human proteins by fusing different features. SC Rep., 2016, 6, 34817.
[80]
Hardy, L.W.; Peet, N.P. The multiple orthogonal tools approach to define molecular causation in the validation of druggable targets. Drug Discov. Today, 2004, 9, 117-126.
[81]
Yao, L.; Rzhetsky, A. Quantitative systems-level determinants of human genes targeted by successful drugs. Genome Res., 2008, 18, 206-213.
[82]
Costa, P.R.; Acencio, M.L.; Lemke, N. A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data. BMC Genomics, 2010, 11, S9.
[83]
Kumari, P.; Nath, A.; Chaube, R. Identification of human drug targets using machine-learning algorithms. Comput. Biol. Med., 2015, 56, 175-181.
[84]
Jamali, A.A.; Ferdousi, R.; Razzaghi, S.; Li, J.; Safdari, R.; Ebrahimie, E. DrugMiner: Comparative analysis of machine learning algorithms for prediction of potential druggable proteins. Drug Discov. Today, 2016, 21, 718-724.
[85]
Jeon, J.; Nim, S.; Teyra, J.; Datti, A.; Wrana, J.L.; Sidhu, S.S.; Moffat, J.; Kim, P.M. A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening. Genome Med., 2014, 6, 57.
[86]
Tang, H.; Su, Z.D.; Wei, H.H.; Chen, W.; Lin, H. Prediction of cell-penetrating peptides with feature selection techniques. Biochem. Biophys. Res. Commun., 2016, 477, 150-154.
[87]
(a) Feng, P.M.; Hao, L.; Wei, C. Identification of antioxidants from sequence information using naïve bayes. Comput. Math. Methods Med., 2013, 2013, 567529.
[88]
Feng, P.M.; Ding, H.; Chen, W.; Lin, H. naïve bayes classifier with feature selection to identify phage virion proteins. Comput. Math. Methods Med., 2013, 2013, 530696.
