Chemoinformatics and advanced machine learning perspectives: complex computational methods and collaborative techniques
"This book is a timely compendium of key elements that are crucial for the study of machine learning in chemoinformatics, giving an overview of current research in machine learning and their applications to chemoinformatics tasks" - Provided by publisher
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey ; New York
Medical Information Science Reference
[2011]
|
| Schriftenreihe: | Premier reference source
|
| Schlagworte: | |
| Online-Zugang: | DE-706 DE-1049 DE-898 DE-1050 DE-83 Volltext |
| Zusammenfassung: | "This book is a timely compendium of key elements that are crucial for the study of machine learning in chemoinformatics, giving an overview of current research in machine learning and their applications to chemoinformatics tasks" - Provided by publisher |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 1 Online-Ressource (xvii, 400 Seiten) |
| ISBN: | 9781615209125 |
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| contents | 1. Graph kernels for chemoinformatics / Hisashi Kashima, Hiroto Saigo, Masahiro Hattori, Koji Tsuda -- 2. Optimal assignment kernels for ADME in silico prediction / Holger Fr ohlich -- 3. 3D ligand-based virtual screening with support vector machines / Jean-Philippe Vert -- 4. A simulation study of the use of similarity fusion for virtual screening / Martin Whittle, Valerie Gillet, Peter Willett -- 5. Structure-activity relationships by autocorrelation descriptors and genetic algorithms / Viviana Consonni, Roberto Todeschini -- 6. Graph mining in chemoinformatics / Hiroto Saigo, Koji Tsuda -- 7. Protein homology analysis for function prediction with parallel sub-graph isomorphism / Alper K u c ukural, Andras Szilagyi, O. Sezerman, Yang Zhang -- 8. Advanced PLS techniques in chemometrics and their applications to molecular design / Kiyoshi Hasegawa, Kimito Funatsu -- 9. Nonlinear partial least squares : an overview / Roman Rosipal -- 10. Virtual screening methods based on Bayesian statistics / Martin Vogt, J urgen Bajorath -- 11. Learning binding affinity from augmented high throughput screening data / Nicos Angelopoulos, Andreas Hadjiprocopis, Malcolm Walkinshaw -- 12. Application of machine leaning in drug discovery and development / Shuxing Zhang -- 13. Learning and prediction of complex molecular structure-property relationships / Rahul Singh -- 14. Learning methodologies for detection and classification of mutagens / Huma Lodhi -- 15. Brain-like processing and classification of chemical data / Michael Schmuker, Gisbert Schneider -- 16. Prediction of compound-protein interactions with machine learning methods / Yoshihiro Yamanishi, Hisashi Kashima -- 17. Chemoinformatics on metabolic pathways / Masahiro Hattori, Masaaki Kotera |
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| spelling | Chemoinformatics and advanced machine learning perspectives complex computational methods and collaborative techniques Huma Lodhi (Imperial College, UK), Yoshihiro Yamanashi (Mines ParisTech—Institut Curie—Inserm U900, France) Hershey ; New York Medical Information Science Reference [2011] © 2011 1 Online-Ressource (xvii, 400 Seiten) txt rdacontent c rdamedia cr rdacarrier Premier reference source Includes bibliographical references and index 1. Graph kernels for chemoinformatics / Hisashi Kashima, Hiroto Saigo, Masahiro Hattori, Koji Tsuda -- 2. Optimal assignment kernels for ADME in silico prediction / Holger Fr ohlich -- 3. 3D ligand-based virtual screening with support vector machines / Jean-Philippe Vert -- 4. A simulation study of the use of similarity fusion for virtual screening / Martin Whittle, Valerie Gillet, Peter Willett -- 5. Structure-activity relationships by autocorrelation descriptors and genetic algorithms / Viviana Consonni, Roberto Todeschini -- 6. Graph mining in chemoinformatics / Hiroto Saigo, Koji Tsuda -- 7. Protein homology analysis for function prediction with parallel sub-graph isomorphism / Alper K u c ukural, Andras Szilagyi, O. Sezerman, Yang Zhang -- 8. Advanced PLS techniques in chemometrics and their applications to molecular design / Kiyoshi Hasegawa, Kimito Funatsu -- 9. Nonlinear partial least squares : an overview / Roman Rosipal -- 10. Virtual screening methods based on Bayesian statistics / Martin Vogt, J urgen Bajorath -- 11. Learning binding affinity from augmented high throughput screening data / Nicos Angelopoulos, Andreas Hadjiprocopis, Malcolm Walkinshaw -- 12. Application of machine leaning in drug discovery and development / Shuxing Zhang -- 13. Learning and prediction of complex molecular structure-property relationships / Rahul Singh -- 14. Learning methodologies for detection and classification of mutagens / Huma Lodhi -- 15. Brain-like processing and classification of chemical data / Michael Schmuker, Gisbert Schneider -- 16. Prediction of compound-protein interactions with machine learning methods / Yoshihiro Yamanishi, Hisashi Kashima -- 17. Chemoinformatics on metabolic pathways / Masahiro Hattori, Masaaki Kotera "This book is a timely compendium of key elements that are crucial for the study of machine learning in chemoinformatics, giving an overview of current research in machine learning and their applications to chemoinformatics tasks" - Provided by publisher Cheminformatics Machine learning Computational chemistry (DE-588)4290091-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Computational chemistry (DE-588)4290091-8 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Lodhi, Huma M. (DE-588)1130686019 edt Yamanashi, Yoshihiro (DE-588)1130686132 edt Erscheint auch als Druck-Ausgabe 978-1-61520-911-8 Erscheint auch als Druck-Ausgabe 1-61520-911-5 Erscheint auch als Druck-Ausgabe 978-1-61692-368-6 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-61520-911-8 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Chemoinformatics and advanced machine learning perspectives complex computational methods and collaborative techniques 1. Graph kernels for chemoinformatics / Hisashi Kashima, Hiroto Saigo, Masahiro Hattori, Koji Tsuda -- 2. Optimal assignment kernels for ADME in silico prediction / Holger Fr ohlich -- 3. 3D ligand-based virtual screening with support vector machines / Jean-Philippe Vert -- 4. A simulation study of the use of similarity fusion for virtual screening / Martin Whittle, Valerie Gillet, Peter Willett -- 5. Structure-activity relationships by autocorrelation descriptors and genetic algorithms / Viviana Consonni, Roberto Todeschini -- 6. Graph mining in chemoinformatics / Hiroto Saigo, Koji Tsuda -- 7. Protein homology analysis for function prediction with parallel sub-graph isomorphism / Alper K u c ukural, Andras Szilagyi, O. Sezerman, Yang Zhang -- 8. Advanced PLS techniques in chemometrics and their applications to molecular design / Kiyoshi Hasegawa, Kimito Funatsu -- 9. Nonlinear partial least squares : an overview / Roman Rosipal -- 10. Virtual screening methods based on Bayesian statistics / Martin Vogt, J urgen Bajorath -- 11. Learning binding affinity from augmented high throughput screening data / Nicos Angelopoulos, Andreas Hadjiprocopis, Malcolm Walkinshaw -- 12. Application of machine leaning in drug discovery and development / Shuxing Zhang -- 13. Learning and prediction of complex molecular structure-property relationships / Rahul Singh -- 14. Learning methodologies for detection and classification of mutagens / Huma Lodhi -- 15. Brain-like processing and classification of chemical data / Michael Schmuker, Gisbert Schneider -- 16. Prediction of compound-protein interactions with machine learning methods / Yoshihiro Yamanishi, Hisashi Kashima -- 17. Chemoinformatics on metabolic pathways / Masahiro Hattori, Masaaki Kotera Cheminformatics Machine learning Computational chemistry (DE-588)4290091-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4290091-8 (DE-588)4193754-5 (DE-588)4143413-4 |
| title | Chemoinformatics and advanced machine learning perspectives complex computational methods and collaborative techniques |
| title_auth | Chemoinformatics and advanced machine learning perspectives complex computational methods and collaborative techniques |
| title_exact_search | Chemoinformatics and advanced machine learning perspectives complex computational methods and collaborative techniques |
| title_full | Chemoinformatics and advanced machine learning perspectives complex computational methods and collaborative techniques Huma Lodhi (Imperial College, UK), Yoshihiro Yamanashi (Mines ParisTech—Institut Curie—Inserm U900, France) |
| title_fullStr | Chemoinformatics and advanced machine learning perspectives complex computational methods and collaborative techniques Huma Lodhi (Imperial College, UK), Yoshihiro Yamanashi (Mines ParisTech—Institut Curie—Inserm U900, France) |
| title_full_unstemmed | Chemoinformatics and advanced machine learning perspectives complex computational methods and collaborative techniques Huma Lodhi (Imperial College, UK), Yoshihiro Yamanashi (Mines ParisTech—Institut Curie—Inserm U900, France) |
| title_short | Chemoinformatics and advanced machine learning perspectives |
| title_sort | chemoinformatics and advanced machine learning perspectives complex computational methods and collaborative techniques |
| title_sub | complex computational methods and collaborative techniques |
| topic | Cheminformatics Machine learning Computational chemistry (DE-588)4290091-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Cheminformatics Machine learning Computational chemistry Maschinelles Lernen Aufsatzsammlung |
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