000 06497nam a2200769 i 4500
001 6949409
003 IEEE
005 20200413152915.0
006 m eo d
007 cr cn |||m|||a
008 141120s2015 caua foab 000 0 eng d
020 _a9781627054515
_qebook
020 _z9781627054508
_qprint
024 7 _a10.2200/S00600ED1V01Y201409WEB009
_2doi
035 _a(CaBNVSL)swl00404343
035 _a(OCoLC)896434163
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aRM301.25
_b.C447 2015
060 4 _aQV 744
_bC447s 2015
082 0 4 _a615.19
_223
090 _a
_bMoCl
_e10.2200/S00600ED1V01Y201409WEB009
100 1 _aChen, Bin,
_d1983-,
_eauthor.
245 1 0 _aSemantic breakthrough in drug discovery /
_cBin Chen, Huijun Wang, Ying Ding, David Wild.
264 1 _aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool,
_c2015.
300 _a1 PDF (ix, 132 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on the semantic web, theory and technology,
_x2160-472X ;
_v# 9
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
504 _aIncludes bibliographical references (pages 113-129).
505 0 _a1. Introduction -- 1.1 Background -- 1.2 Data representation in the Semantic Web -- 1.3 Data query, management, and integration -- 1.4 Knowledge discovery in semantically integrated datasets -- 1.5 Chem2Bio2RDF --
505 8 _a2. Data representation and integration using RDF -- 2.1 Background -- 2.2 Methods -- 2.3 Discussion -- 2.4 Conclusion --
505 8 _a3. Data representation and integration using OWL -- 3.1 Introduction -- 3.2 System and methods -- 3.3 Implementation -- 3.4 Use cases -- 3.5 Discussion -- 3.6 Conclusion --
505 8 _a4. Finding complex biological relationships in PubMed articles using Bio-LDA -- 4.1 Introduction -- 4.2 Materials and methods -- 4.2.1 Databases -- 4.2.2 Bio-LDA -- 4.3 Experimental results -- 4.3.1 Analyzing the Bio-LDA model results -- 4.3.2 Comparing the Bio-LDA and LDA models -- 4.3.3 Identification of bio-term relationships within topics -- 4.3.4 Discovery of bio-term associations -- 4.4 Application tools -- 4.4.1 Literature Association Score Calculator (LASC) -- 4.4.2 Associated Bio-Terms Finder (ABTF) -- 4.5 Conclusion --
505 8 _a5. Integrated semantic approach for systems chemical biology knowledge discovery -- 5.1 Introduction -- 5.2 Datasets -- 5.3 Methods -- 5.3.1 Association prediction -- 5.3.2 Association search -- 5.3.3 Association exploration -- 5.3.4 Connectivity-map generation -- 5.3.5 Chem2Bio2RDF extension -- 5.4 Application tools -- 5.4.1 Association predictor -- 5.4.2 Association searcher -- 5.4.3 Association explorer -- 5.5 Use cases -- 5.5.1 Identifying potential drugs for a target -- 5.5.2 Investigating drug polypharmacology using association search -- 5.5.3 Building a disease-specific drug-protein connectivity map -- 5.5.4 Association search for discovery compounds -- 5.6 Conclusion --
505 8 _a6. Semantic link association prediction -- 6.1 Introduction -- 6.2 Materials and methods -- 6.2.1 Network building -- 6.2.2 Drug target pair preparation -- 6.2.3 Path finding -- 6.2.4 Statistical model -- 6.2.5 Model evaluation -- 6.2.6 Assess drug similarity -- 6.3 Results -- 6.3.1 Semantic linked data -- 6.3.2 Pattern score distribution -- 6.3.3 Pattern importance -- 6.3.4 Association scores of drug target pairs -- 6.3.5 Comparison with connectivity maps -- 6.3.6 Assessing drug similarity from biological function -- 6.4 Web services -- 6.5 Discussion --
505 8 _a7. Conclusions -- References -- Authors' biographies.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _aThe current drug development paradigm, sometimes expressed as, "one disease, one target, one drug," is under question, as relatively few drugs have reached the market in the last two decades. Meanwhile, the research focus of drug discovery is being placed on the study of drug action on biological systems as a whole, rather than on individual components of such systems. The vast amount of biological information about genes and proteins and their modulation by small molecules is pushing drug discovery to its next critical steps, involving the integration of chemical knowledge with these biological databases. Systematic integration of these heterogeneous datasets and the provision of algorithms to mine the integrated datasets would enable investigation of the complex mechanisms of drug action; however, traditional approaches face challenges in the representation and integration of multi-scale datasets, and in the discovery of underlying knowledge in the integrated datasets. The Semantic Web, envisioned to enable machines to understand and respond to complex human requests and to retrieve relevant, yet distributed, data, has the potential to trigger system-level chemical-biological innovations. Chem2Bio2RDF is presented as an example of utilizing Semantic Web technologies to enable intelligent analyses for drug discovery.
530 _aAlso available in print.
588 _aTitle from PDF title page (viewed on November 20, 2014).
630 0 0 _aChem2Bio2RDF.
650 0 _aDrug development.
650 0 _aDrugs
_xResearch.
650 0 _aSemantic Web.
650 2 _aDrug Discovery.
653 _adrug discovery
653 _asemantic data integration
653 _asemantic analytics
653 _asemantic graph mining
653 _asemantic prediction
700 1 _aWang, Huijun.,
_eauthor.
700 1 _aDing, Ying,
_d1955-,
_eauthor.
700 1 _aWild, David.,
_eauthor.
776 0 8 _iPrint version:
_z9781627054508
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on the semantic web, theory and technology ;
_v# 9.
_x2160-472X
856 4 0 _3Abstract with links to full text
_uhttp://dx.doi.org/10.2200/S00600ED1V01Y201409WEB009
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6949409
999 _c562097
_d562097