• 1
    Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42, 724733.
  • 2
    Hurle MR, Yang L, Xie Q, Rajpal DK, Sanseau P & Agarwal P (2013) Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther 93, 335341.
  • 3
    Wu Z, Wang Y & Chen L (2013) Network-based drug repositioning. Mol BioSyst 9, 12681281.
  • 4
    Lamb J (2007) The Connectivity Map: a new tool for biomedical research. Nat Rev Cancer 7, 5460.
  • 5
    Qu XA & Rajpal DK (2012) Applications of Connectivity Map in drug discovery and development. Drug Discov Today 17, 12891298.
  • 6
    Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J & Butte AJ (2011) Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med 3, 96ra77.
  • 7
    Siu FM, Ma DL, Cheung YW, Lok CN, Yan K, Yang Z, Yang M, Xu S, Ko BC, He QY et al. (2008) Proteomic and transcriptomic study on the action of a cytotoxic saponin (Polyphyllin D): induction of endoplasmic reticulum stress and mitochondria-mediated apoptotic pathways. Proteomics 8, 31053117.
  • 8
    Kunkel SD, Suneja M, Ebert SM, Bongers KS, Fox DK, Malmberg SE, Alipour F, Shields RK & Adams CM (2011) mRNA expression signatures of human skeletal muscle atrophy identify a natural compound that increases muscle mass. Cell Metab 13, 627638.
  • 9
    Hassane DC, Guzman ML, Corbett C, Li X, Abboud R, Young F, Liesveld JL, Carroll M & Jordan CT (2008) Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data. Blood 111, 56545662.
  • 10
    Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN et al. (2006) The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 19291935.
  • 11
    Miller RM & Federoff HJ (2005) Altered gene expression profiles reveal similarities and differences between Parkinson disease and model systems. Neuroscientist 11, 539549.
  • 12
    Exner N, Lutz AK, Haass C & Winklhofer KF (2012) Mitochondrial dysfunction in Parkinson's disease: molecular mechanisms and pathophysiological consequences. EMBO J 31, 30383062.
  • 13
    Sherer TB, Betarbet R, Testa CM, Seo BB, Richardson JR, Kim JH, Miller GW, Yagi T, Matsuno-Yagi A & Greenamyre JT (2003) Mechanism of toxicity in rotenone models of Parkinson's disease. J Neurosci 23, 1075610764.
  • 14
    Amberger J, Bocchini C & Hamosh A (2011) A new face and new challenges for Online Mendelian Inheritance in Man (OMIM®). Hum Mutat 32, 564567.
  • 15
    Taccioli C, Tegnér J, Maselli V, Gomez-Cabrero D, Altobelli G, Emmett W, Lescai F, Gustincich S & Stupka E (2011) ParkDB: a Parkinson's disease gene expression database. Database (Oxford) 2011, 16 (bar007).
  • 16
    Davis AP, King BL, Mockus S, Murphy CG, Saraceni-Richards C, Rosenstein M, Wiegers T & Mattingly CJ (2011) The Comparative Toxicogenomics Database: update 2011. Nucleic Acids Res 39, D1067D1072.
  • 17
    Pacey S, Wilson RH, Walton M, Eatock MM, Hardcastle A, Zetterlund A, Arkenau HT, Moreno-Farre J, Banerji U, Roels B et al. (2011) A phase I study of the heat shock protein 90 inhibitor alvespimycin (17-DMAG) given intravenously to patients with advanced solid tumors. Clin Cancer Res 17, 15611570.
  • 18
    Lancet JE, Gojo I, Burton M, Quinn M, Tighe SM, Kersey K, Zhong Z, Albitar MX, Bhalla K, Hannah AL et al. (2010) Phase I study of the heat shock protein 90 inhibitor alvespimycin (KOS-1022, 17-DMAG) administered intravenously twice weekly to patients with acute myeloid leukemia. Leukemia 24, 699705.
  • 19
    Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M et al. (2013) NCBI GEO: archive for functional genomics data sets – update. Nucleic Acids Res 41, D991D995.
  • 20
    Tusher VG, Tibshirani R & Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci 98, 51165121.
  • 21
    Ruan J, Dean AK & Zhang W (2010) A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst Biol 4, 8.
  • 22
    Köhler S, Bauer S, Horn D & Robinson PN (2008) Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82, 949958.
  • 23
    Adachi H, Katsuno M, Waza M, Minamiyama M, Tanaka F & Sobue G (2009) Heat shock proteins in neurodegenerative diseases: pathogenic roles and therapeutic implications. Int J Hyperthermia 25, 647654.
  • 24
    Dimant H, Ebrahimi-Fakhari D & McLean PJ (2012) Molecular chaperones and co-chaperones in Parkinson disease. Neuroscientist 18, 589601.
  • 25
    Kalia SK, Kalia LV & McLean PJ (2010) Molecular chaperones as rational drug targets for Parkinson's disease therapeutics. CNS Neurol Disord Drug Targets 9, 741753.
  • 26
    Ebrahimi-Fakhari D, Wahlster L & McLean PJ (2011) Molecular chaperones in Parkinson's disease – present and future. J Parkinsons Dis 1, 299320.
  • 27
    Piro RM & Di Cunto F (2012) Computational approaches to disease-gene prediction: rationale, classification and successes. FEBS J 279, 678696.
  • 28
    Uryu K, Richter-Landsberg C, Welch W, Sun E, Goldbaum O, Norris EH, Pham CT, Yazawa I, Hilburger K, Micsenyi M et al. (2006) Convergence of heat shock protein 90 with ubiquitin in filamentous alpha-synuclein inclusions of alpha-synucleinopathies. Am J Pathol 168, 947961.
  • 29
    Moriwaki Y, Kim YJ, Ido Y, Misawa H, Kawashima K, Endo S & Takahashi R (2008) L347P PINK1 mutant that fails to bind to Hsp90/Cdc37 chaperones is rapidly degraded in a proteasome-dependent manner. Neurosci Res 61, 4348.
  • 30
    Wang L, Xie C, Greggio E, Parisiadou L, Shim H, Sun L, Chandran J, Lin X, Lai C, Yang WJ et al. (2008) The chaperone activity of heat shock protein 90 is critical for maintaining the stability of leucine-rich repeat kinase 2. J Neurosci 28, 33843391.
  • 31
    Shen HY, He JC, Wang Y, Huang QY & Chen JF (2005) Geldanamycin induces heat shock protein 70 and protects against MPTP-induced dopaminergic neurotoxicity in mice. J Biol Chem 280, 3996239969.
  • 32
    Auluck PK & Bonini NM (2002) Pharmacological prevention of Parkinson disease in Drosophila. Nat Med 8, 11851186.
  • 33
    Tokui K, Adachi H, Waza M, Katsuno M, Minamiyama M, Doi H, Tanaka K, Hamazaki J, Murata S, Tanaka F et al. (2009) 17-DMAG ameliorates polyglutamine-mediated motor neuron degeneration through well-preserved proteasome function in an SBMA model mouse. Hum Mol Genet 18, 898910.
  • 34
    Betarbet R, Sherer TB, MacKenzie G, Garcia-Osuna M, Panov AV & Greenamyre JT (2000) Chronic systemic pesticide exposure reproduces features of Parkinson's disease. Nat Neurosci 3, 13011306.
  • 35
    Alberio T, Lopiano L & Fasano M (2012) Cellular models to investigate biochemical pathways in Parkinson's disease. FEBS J 279, 11461155.
  • 36
    Sherer TB, Betarbet R, Stout AK, Lund S, Baptista M, Panov AV, Cookson MR & Greenamyre JT (2002) An in vitro model of Parkinson's disease: linking mitochondrial impairment to altered alpha-synuclein metabolism and oxidative damage. J Neurosci 22, 70067015.
  • 37
    Kim YS, Alarcon SV, Lee S, Lee MJ, Giaccone G, Neckers L & Trepel JB (2009) Update on Hsp90 inhibitors in clinical trial. Curr Top Med Chem 9, 14791492.
  • 38
    Moran LB, Duke DC, Deprez M, Dexter DT, Pearce RK & Graeber MB (2006) Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson's disease. Neurogenetics 7, 111.
  • 39
    Lesnick TG, Papapetropoulos S, Mash DC, Ffrench-Mullen J, Shehadeh L, de Andrade M, Henley JR, Rocca WA, Ahlskog JE & Maraganore DM (2007) A genomic pathway approach to a complex disease: axon guidance and Parkinson disease. PLoS Genet 3, e98.
  • 40
    Shi L, Tong W, Fang H, Scherf U, Han J, Puri RK, Frueh FW, Goodsaid FM, Guo L, Su Z et al. (2005) Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential. BMC Bioinformatics 6 (Suppl 2), S12.
  • 41
    Zhang B & Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4, 17.
  • 42
    Smoot ME, Ono K, Ruscheinski J, Wang PL & Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431432.
  • 43
    Le DH & Kwon YK (2012) GPEC: a Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection. Comput Biol Chem 37, 1723.
  • 44
    Zou ZH, Yun Y & Sun JN (2006) Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. J Environ Sci (China) 18, 10201023.
  • 45
    Yang JM, Chen YF, Shen TW, Kristal BS & Hsu DF (2005) Consensus scoring criteria for improving enrichment in virtual screening. J Chem Inf Model 45, 11341146.
  • 46
    Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27, 379423, 623–656.
  • 47
    Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z & Woolsey J (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34, D668D672.
  • 48
    Zhu F, Shi Z, Qin C, Tao L, Liu X, Xu F, Zhang L, Song Y, Liu X, Zhang J et al. (2012) Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res 40, D1128D1136.
  • 49
    Cheng D, Knox C, Young N, Stothard P, Damaraju S & Wishart DS (2008) PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res 36, W399W405.
  • 50
    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT & Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47, 17501759.
  • 51
    Mosmann T (1983) Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. J Immunol Methods 65, 5563.
  • 52
    Gao L, Fang JS, Bai XY, Zhou D, Wang YT, Liu AL & Du GH (2013) In silico target fishing for the potential targets and molecular mechanisms of baicalein as an antiparkinsonian agent: discovery of the protective effects on NMDA receptor-mediated neurotoxicity. Chem Biol Drug Des 81, 675687.
  • 53
    Li XX, He GR, Mu X, Xu B, Tian S, Yu X, Meng FR, Xuan ZH & Du GH (2012) Protective effects of baicalein against rotenone-induced neurotoxicity in PC12 cells and isolated rat brain mitochondria. Eur J Pharmacol 674, 227233.