Literature mining, ontologies and information visualization for drug repurposing

Briefings in Bioinformatics

Volumn 12, Issue 4, 2011, Pages 357-368

  Andronis, Christos ^a   Sharma, Anuj ^a   Virvilis, Vassilis ^a   Deftereos, Spyros ^a   Persidis, Aris ^a  

^a Biovista Inc   (United States)

Author keywords

Drug repurposing; Information visualization; Literature mining; Ontologies

Indexed keywords

ARTICLE; BIOLOGY; DATA MINING; DRUG DEVELOPMENT; DRUG REPOSITIONING; LINGUISTICS; MEDLINE; METHODOLOGY;

COMPUTATIONAL BIOLOGY; DATA MINING; DRUG DISCOVERY; DRUG REPOSITIONING; MEDLINE; VOCABULARY, CONTROLLED;

EID: 79960766975     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbr005     Document Type: Article

References (93)

1. Fleming E, Ma P. Drug life-cycle technologies. Nat Rev Drug Discov 2002;1(10):751-2.
2. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 2004;3(8):673-83.
3. Campas C. Drug repositioning summit: finding new routes to success. Drug News Perspect 2009;22(2):126-8.
4. Iorio F, Bosotti R, Scacheri E, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci USA 2010;107(33):14621-6.
5. Dudley JT, Schadt E, Sirota M, et al. Drug discovery in a multidimensional world: systems, patterns, and networks. J Cardiovasc Transl Res 2010;3(5):438-47.
6. Kotelnikova E, Yuryev A, Mazo I, et al. Computational approaches for drug repositioning and combination therapy design. J Bioinform Comput Biol 2010;8(3):593-606.
7. Cockell SJ, Weile J, Lord P, et al. An integrated dataset for in silico drug discovery. J Integr Bioinform 2010;7(3):116.
8. Pujol A, Mosca R, Farrés J, et al. Unveiling the role of network and systems biology in drug discovery. Trends Pharmacol Sci 2010;31(3):115-23.
9. Hu G, Agarwal P. Human disease-drug network based on genomic expression profiles. PLoS One 2009;4(8):e6536.
10. Kinnings SL, Liu N, Buchmeier N, et al. Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoSComput Biol 2009;5(7):e1000423.
11. 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.
12. Scheiber J, Chen B, Milik M, et al. Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis. J Chem Inf Model 2009;49:308-17.
13. Ha S, Seo YJ, Kwon MS, et al. IDMap: facilitating the detection of potential leads with therapeutic targets. Bioinformatics 2008;24(11):1413-5.
14. Jenssen TK, Laegreid A, Komorowski J, et al. A literature network of human genes for high-throughput analysis of gene expression. Nat Genet 2001;28(1):21-8.
15. Chun HW, Tsuruoka Y, Kim JD, et al. Extraction of gene-disease relations from Medline using domain dictionaries and machine learning. Pac Symp Biocomput 2006; 4-15.
16. Jensen LJ, Saric J, Bork P. Literature mining for the biologist: from information retrieval to biological discovery. Nat Rev Genet 2006;7(2):119-29.
17. Frijters R, van Vugt M, Smeets R, et al. Literature mining for the discovery of hidden connections between drugs, genes and diseases. PLoS Comput Biol 2010;6(9). pii: e1000943.
18. Zhu Q, Lajiness MS, Ding Y, et al. WENDI: a tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications. J Cheminform 2010;2:6.
19. Baker NC, Hemminger BM. Mining connections between chemicals, proteins, and diseases extracted from Medline annotations. J Biomed Inform 2010;43(4):510-9.
20. Agarwal P, Searls DB. Literature mining in support of drug discovery. Brief Bioinform 2008;9(6):479-92.
21. Bodenreider O, Stevens R. Bio-ontologies: current trends and future directions. Brief Bioinform 2006;7(3):256-74.
22. Bard JB, Rhee SY. Ontologies in biology: design, applications and future challenges. Nat Rev Genet 2004;5(3): 213-22.
23. Qu XA, Gudivada RC, Jegga AG, et al. Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships. BMC Bioinform 2009;10(Suppl 5):S4.
24. Swanson DR. Medical literature as a potential source of new knowledge. Bull Med Libr Assoc 1990;78(1):29-37.
25. Swanson DR. Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspect Biol Med 1986;30(1):7-18.
26. Swanson DR. Migraine and magnesium: eleven neglected connections. Perspect Biol Med 1988;31(4):526-57.
27. DiGiacomo RA, Kremer JM, Shah DM. Fish-oil dietary supplementation in patients with Raynaud's phenomenon: a double-blind, controlled, prospective study. Am J Med 1989;86(2):158-64.
28. Schiapparelli P, Allais G, Castagnoli Gabellari I, et al. Non-pharmacological approach to migraine prophylaxis: part II. Neurol Sci 2010;31(Suppl 1):S137-9.
29. Weeber M, Klein H, de Jong-van den Berg LT, et al. Using concepts in literature-based discovery: Simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries. J Am Soc Inform Sci Tech 2001;52:548-57.
30. Weeber M, Kors JA, Mons B. Online tools to support literature-based discovery in the life sciences. Brief Bioinform 2005;6(3):277-86.
31. Smalheiser NR, Swanson DR. Using ARROWSMITH: a computer-assisted approach to formulating and assessing scientific hypotheses. Comput Methods Programs Biomed 1998; 57(3):149-53.
32. Lindsay RK, Gordon MD. Literature-based discovery by lexical statistics. J Am Soc Inform Sci 1999;50:574-87.
33. Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004;32(Database issue):D267-70.
34. Aronson AR. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp 2001;17-21.
35. Weeber M, Vos R, Klein H, et al. Generating hypotheses by discovering implicit associations in the literature: a case report of a search for new potential therapeutic uses for thalidomide. JAmMed Inform Assoc 2003;10(3):252-9.
36. Yetisgen-Yildiz M, Pratt W. Using statistical and knowledge-based approaches for literature-based discovery. J Biomed Inform 2006;39(6):600-11.
37. Fuller SS, Revere D, Bugni PF, et al. A knowledgebase system to enhance scientific discovery: Telemakus. Biomed Digit Libr 2004;1(1):2.
38. Medical Subject headings factsheet. 2010. http://www.nlm.nih.gov/pubs/factsheets/mesh.html (10 January 2011, date last accessed).
39. van der Eijk CC, van Mulligen EM, Kors JA, et al. Constructing an associative concept space for literature-based discovery. J Am Soc Inform Sci Technol 2004; 55(5):436-44.
40. Wren JD, Bekeredjian R, Stewart JA, et al. Knowledge discovery by automated identification and ranking of implicit relationships. Bioinformatics 2004;20(3):389-98.
41. Narayanasamy V, Mukhopadhyay S, Palakal M, et al. TransMiner: mining transitive associations among biological objects from text. J Biomed Sci 2004;11(6):864-73.
42. Ahlers CB, Hristovski D, Kilicoglu H, et al. Using the literature-based discovery paradigm to investigate drug mechanisms. AMIA Annu Symp Proc 2007;6-10.
43. Petric I, Urbancic T, Cestnik B, et al. Literature mining method RaJoLink for uncovering relations between biomedical concepts. J Biomed Inform 2009;42(2):219-27.
44. Hristovski D, Peterlin B, Mitchell JA, et al. Using literature-based discovery to identify disease candidate genes. Int J Med Inform 2005;74(2-4):289-98.
45. Baker NC, Hemminger BM. Mining connections between chemicals, proteins, and diseases extracted from Medline annotations. J Biomed Inform 2010;43(4):510-9.
46. Srinivasan P, Libbus B. Mining MEDLINE for implicit links between dietary substances and diseases. Bioinformatics 2004; 20(Suppl 1):i290-6.
47. Kostoff RN, Block JA, Stump JA, et al. Information content in Medline record fields. Int J Med Inform 2004;73(6):515-27.
48. UniProt Consortium. The Universal Protein Resource (UniProt) in 2010. Nucleic Acids Res 2010;38(Database issue):D142-8.
49. NCBI Entrez Gene. http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene (10 January 2011, date last accessed).
50. Matthews L, Gopinath G, Gillespie M, et al. Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 2009;37(Database issue):D619-22.
51. Zweigenbaum P, Demner-Fushman D, Yu H, et al. Frontiers of biomedical text mining: current progress. Brief Bioinform 2007;8(5):358-75.
52. Cohen KB, Hunter L. Getting started in text mining. PLoS Comput Biol 2008;4(1):e20.
53. Cohen AM, Hersh WR. A survey of current work in biomedical text mining. Brief Bioinform 2005;6(1):57-71.
54. Hatzivassiloglou V, Duboué PA, Rzhetsky A. Disambiguating proteins, genes, and RNA in text: a machine learning approach. Bioinformatics 2001;17(Suppl 1): S97-106.
55. Tveit A, Saetre R, Laegreid A, et al. ProtChew: Automatic extraction of protein names from biomedical literature. In Proceedings of the 21st international Conference on Data EngineeringWorkshops 2005;1161.
56. Schuemie MJ, Kors JA, Mons B. Word sense disambiguation in the biomedical domain: an overview. JComputBiol 2005;12(5):554-65.
57. Alexopoulou D, Andreopoulos B, Dietze H, et al. Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy. BMC Bioinformatics 2009;10:28.
58. Xu H, Markatou M, Dimova R, et al. Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues. BMC Bioinformatics 2006;7:334.
59. Chen ES, Hripcsak G, Xu H, et al. Automated acquisition of disease drug knowledge from biomedical and clinical documents: an initial study. J Am Med Inform Assoc 2008;15(1): 87-98.
60. Ontologies in Bioinformatics EMBER ProjectWorkbook on Bioinformatics. (T. Atttwood, ed.) Vary, J. P. (Ed.), 2000.
61. Stevens R, Aranguren ME, Wolstencroft K, et al. Using OWL to model biological knowledge. Int J Hum-Comput Stud 2007;65:7583-94.
62. Schulz S, Stenzhorn H, Boeker M, et al. Strengths and limitations of formal ontologies in the biomedical domain. RECIISRev ElectronComun Inf Inov Saude 2009;3(1):31-45.
63. http://www.w3.org/2001/sw/(6 February 2011, date last accessed).
64. Web Ontology Language. http://www.w3.org/TR/2004/REC-owl-guide-20040210/(10 January 2011, date last accessed).
65. Resource Description Framework. http://www.w3.org/TR/PR-rdf-syntax/(10 January 2011, date last accessed).
66. PROTEGE. http://protege.stanford.edu/(10 January 2011, date last accessed).
67. FACT++. http://owl.man.ac.uk/factplusplus/.
68. HermiT. http://hermit-reasoner.com/(10 January 2011, date last accessed).
69. Ashburner M, Mungall CJ, Lewis SE. Ontologies for biologists: a community model for the annotation of genomic data. Cold SpringHarb Symp Quant Biol 2003;68:227-35.
70. Hoehndorf R, Oellrich A, Dumontier M, et al. Relations as patterns: bridging the gap between OBO and OWL. BMC Bioinformatics 2010;11:441.
71. Pesquita C, Faria D, Falcão AO, et al. Semantic similarity in biomedical ontologies. PLoS Comput Biol 2009;5(7): e1000443.
72. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000;25(1):25-9.
73. Campbell SJ, Gaulton A, Marshall J, et al. Visualizing the drug target landscape. Drug Discov Today 2010;15(1-2):3-15.
74. Keiser MJ, Setola V, Irwin JJ, et al. Predicting new molecular targets for known drugs. Nature 2009;462(7270):175-181.
75. Li J, Zhu X, Chen JY. Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. PLoS Comput Biol 2009;5(7): e1000450.
76. Li YY, An J, Jones SJ. A large-scale computational approach to drug repositioning. Genome Inform 2006;17(2):239-47.
77. Katifori A, Halatsis C, Lepouras G, et al. Ontology Visualization Methods-A Survey. ACM Comput Surveys 2007;39(4). Article 10.
78. Online Mendelian Inheritance in Man, OMIM (TM). McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University (Baltimore, MD) and National Center for Biotechnology Information, National Library of Medicine (Bethesda, MD). http://www.ncbi.nlm.nih.gov/omim/(10 January 2011, date last accessed).
79. Brown KR, Jurisica I. Online predicted human interaction database. Bioinformatics 2005;21(9):2076-82.
80. Jimeno-Yepes A, Berlanga-Llavori R, Rebholz-Schuhmann D. Exploitation of ontological resources for scientific literature analysis: searching genes and related diseases. Conf Proc IEEE EngMed Biol Soc 2009;2009:7073-8.
81. Spasic I, Ananiadou S, McNaught J, et al. Text mining and ontologies in biomedicine: making sense of raw text. Brief Bioinform 2005;6(3):239-51.
82. Qu XA, Gudivada RC, Jegga AG, et al. Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships. BMC Bioinformatics 2009;10(Suppl 5):S4.
83. Kö hler J, Baumbach J, Taubert J, et al. Graph-based analysis and visualization of experimental results with ONDEX. Bioinformatics 2006;22(11):1383-90.
84. Choi J, Davis MJ, Newman AF, et al. A semantic web ontology for small molecules and their biological targets. J Chem Inf Model 2010;50(5):732-41.
85. Cure O, Giroud J. Ontology-based Data Quality enhancement for Drug Databases. WWW2007, May 8-12, 2007, Banff, Canada.
86. Cerami E, Demir E, Schultz N, et al. Automated network analysis identifies core pathways in glioblastoma. PLoS One 2010;5(2):e8918.
87. Campillos M, Kuhn M, Gavin AC, et al. Drug target identification using side-effect similarity. Science 2008;321(5886): 263-6.
88. Paolini GV, Shapland RH, van Hoorn WP, et al. Global mapping of pharmacological space. Nat Biotechnol 2006; 24(7):805-15.
89. Korbel JO, Doerks T, Jensen LJ, et al. Systematic association of genes to phenotypes by genome and literature mining. PLoS Biol 2005;3(5):e134.
90. Kotelnikova E, Yuryev A, Mazo I, et al. Computational approaches for drug repositioning and combination therapy design. J Bioinform Comput Biol 2010;8(3):593-606.
91. Persidis A, Deftereos S, Persidis A. Systems literature analysis. Pharmacogenomics 2004;5(7):943-7.
92. Deftereos SN, Andronis C, Virvillis V, et al. Dimebon Ameliorates Disease Severity in the MOG-Induced Experimental Allergic Encephalomyelitis Animal Model of Progressive Multiple Sclerosis. American Neurological Association 135th Annual Meeting, September 12-15 2010, San Francisco, CA, USA.
93. Deftereos SN, Andronis C, Sharma A, et al. Systematic Drug Repurposing for CNS Indications: Account of Two Successful Case Studies. A. American Neurological Association 135th Annual Meeting, September 12-15 2010, San Francisco, CA, USA.