Semantic breakthrough in drug discovery:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
San Rafael, California
Morgan & Claypool Publishers
[2015]
|
| Series: | Synthesis lectures on the semantic web, theory and technology
#9 |
| Subjects: | |
| Item Description: | Online resource; title from PDF title page (Morgan & Claypool, viewed on November 20, 2014) |
| Physical Description: | 1 online resource (ix, 132 pages) illustrations |
| ISBN: | 9781627054515 1627054510 1627054502 9781627054508 |
Staff View
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| 245 | 1 | 0 | |a Semantic breakthrough in drug discovery |c Bin Chen, Huijun Wang, Ying Ding, David Wild |
| 264 | 1 | |a San Rafael, California |b Morgan & Claypool Publishers |c [2015] | |
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| 500 | |a Online resource; title from PDF title page (Morgan & Claypool, viewed on November 20, 2014) | ||
| 505 | 8 | |a Data representation and integration using RDF -- Data representation and integration using OWL -- Finding complex biological relationships in PubMed articles using Bio-LDA -- Integrated semantic approach for systems chemical biology knowledge discovery -- Semantic link association prediction | |
| 505 | 8 | |a 1. 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 | |a 2. Data representation and integration using RDF -- 2.1 Background -- 2.2 Methods -- 2.3 Discussion -- 2.4 Conclusion | |
| 505 | 8 | |a 3. 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 | |a 4. 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 | |a 5. 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 | |a 6. 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 | |a 7. Conclusions -- References -- Authors' biographies | |
| 505 | 8 | |a The 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 | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Drug development |2 fast | |
| 650 | 7 | |a Drugs / Research |2 fast | |
| 650 | 7 | |a Semantic Web |2 fast | |
| 650 | 4 | |a Drug Discovery / methods | |
| 650 | 4 | |a Internet | |
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| 650 | 4 | |a Drug development |a Drugs |x Research |a Semantic Web |a Data mining | |
| 700 | 1 | |a Wang, Huijun |e Sonstige |4 oth | |
| 700 | 1 | |a Ding, Ying |e Sonstige |4 oth | |
| 700 | 1 | |a Wild, David |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Chen, Bin, 1970- |t Semantic breakthrough in drug discovery |d San Rafael, California : Morgan & Claypool Publishers, [2015] |z 9781627054508 |
| 912 | |a ZDB-4-NLEBK | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-029761419 | ||
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| any_adam_object | |
| author | Chen, Bin 1983- |
| author_facet | Chen, Bin 1983- |
| author_role | aut |
| author_sort | Chen, Bin 1983- |
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| bvnumber | BV044358788 |
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| contents | Data representation and integration using RDF -- Data representation and integration using OWL -- Finding complex biological relationships in PubMed articles using Bio-LDA -- Integrated semantic approach for systems chemical biology knowledge discovery -- Semantic link association prediction 1. 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. 2. Data representation and integration using RDF -- 2.1 Background -- 2.2 Methods -- 2.3 Discussion -- 2.4 Conclusion 3. 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 4. 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 5. 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 6. 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 7. Conclusions -- References -- Authors' biographies The 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 |
| ctrlnum | (ZDB-4-NLEBK)ocn896434163 (OCoLC)896434163 (DE-599)BVBBV044358788 |
| dewey-full | 615.19 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 615 - Pharmacology and therapeutics |
| dewey-raw | 615.19 |
| dewey-search | 615.19 |
| dewey-sort | 3615.19 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| format | Electronic eBook |
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| id | DE-604.BV044358788 |
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| indexdate | 2024-07-10T07:50:43Z |
| institution | BVB |
| isbn | 9781627054515 1627054510 1627054502 9781627054508 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029761419 |
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| psigel | ZDB-4-NLEBK |
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| series2 | Synthesis lectures on the semantic web, theory and technology |
| spelling | Chen, Bin 1983- Verfasser aut Semantic breakthrough in drug discovery Bin Chen, Huijun Wang, Ying Ding, David Wild San Rafael, California Morgan & Claypool Publishers [2015] © 2015 1 online resource (ix, 132 pages) illustrations txt rdacontent c rdamedia cr rdacarrier Synthesis lectures on the semantic web, theory and technology #9 Online resource; title from PDF title page (Morgan & Claypool, viewed on November 20, 2014) Data representation and integration using RDF -- Data representation and integration using OWL -- Finding complex biological relationships in PubMed articles using Bio-LDA -- Integrated semantic approach for systems chemical biology knowledge discovery -- Semantic link association prediction 1. 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. 2. Data representation and integration using RDF -- 2.1 Background -- 2.2 Methods -- 2.3 Discussion -- 2.4 Conclusion 3. 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 4. 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 5. 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 6. 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 7. Conclusions -- References -- Authors' biographies The 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 Data mining fast Drug development fast Drugs / Research fast Semantic Web fast Drug Discovery / methods Internet Datasets as Topic Data Mining Drug development Drugs Research Semantic Web Data mining Wang, Huijun Sonstige oth Ding, Ying Sonstige oth Wild, David Sonstige oth Erscheint auch als Druck-Ausgabe Chen, Bin, 1970- Semantic breakthrough in drug discovery San Rafael, California : Morgan & Claypool Publishers, [2015] 9781627054508 |
| spellingShingle | Chen, Bin 1983- Semantic breakthrough in drug discovery Data representation and integration using RDF -- Data representation and integration using OWL -- Finding complex biological relationships in PubMed articles using Bio-LDA -- Integrated semantic approach for systems chemical biology knowledge discovery -- Semantic link association prediction 1. 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. 2. Data representation and integration using RDF -- 2.1 Background -- 2.2 Methods -- 2.3 Discussion -- 2.4 Conclusion 3. 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 4. 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 5. 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 6. 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 7. Conclusions -- References -- Authors' biographies The 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 Data mining fast Drug development fast Drugs / Research fast Semantic Web fast Drug Discovery / methods Internet Datasets as Topic Data Mining Drug development Drugs Research Semantic Web Data mining |
| title | Semantic breakthrough in drug discovery |
| title_auth | Semantic breakthrough in drug discovery |
| title_exact_search | Semantic breakthrough in drug discovery |
| title_full | Semantic breakthrough in drug discovery Bin Chen, Huijun Wang, Ying Ding, David Wild |
| title_fullStr | Semantic breakthrough in drug discovery Bin Chen, Huijun Wang, Ying Ding, David Wild |
| title_full_unstemmed | Semantic breakthrough in drug discovery Bin Chen, Huijun Wang, Ying Ding, David Wild |
| title_short | Semantic breakthrough in drug discovery |
| title_sort | semantic breakthrough in drug discovery |
| topic | Data mining fast Drug development fast Drugs / Research fast Semantic Web fast Drug Discovery / methods Internet Datasets as Topic Data Mining Drug development Drugs Research Semantic Web Data mining |
| topic_facet | Data mining Drug development Drugs / Research Semantic Web Drug Discovery / methods Internet Datasets as Topic Data Mining Drug development Drugs Research Semantic Web Data mining |
| work_keys_str_mv | AT chenbin semanticbreakthroughindrugdiscovery AT wanghuijun semanticbreakthroughindrugdiscovery AT dingying semanticbreakthroughindrugdiscovery AT wilddavid semanticbreakthroughindrugdiscovery |