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2010-01-22
Learning and Inference in Computational Systems Biology - de Neil D. Lawrence, Mark Girolami, Magnus Rattray, Guido Sanguinetti (Author)
Caractéristiques Learning and Inference in Computational Systems Biology
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| Le Titre Du Fichier | Learning and Inference in Computational Systems Biology |
| Publié Le | 2010-01-22 |
| Traducteur | Ileana Sibel |
| Quantité de Pages | 509 Pages |
| Taille du fichier | 77.10 MB |
| Langage | Français & Anglais |
| Éditeur | José Corti |
| ISBN-10 | 6650483216-MLA |
| Format de e-Book | PDF AMZ EPub HWP WRI |
| Auteur | Neil D. Lawrence, Mark Girolami, Magnus Rattray, Guido Sanguinetti |
| EAN | 177-8437874444-OLT |
| Nom de Fichier | Learning-and-Inference-in-Computational-Systems-Biology.pdf |
Télécharger Learning and Inference in Computational Systems Biology Livre PDF Gratuit
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phenomena emerging from these systems are tightly linked to their organizational properties This raises methodological challenges which are precisely the focus of studyofthemachinelearningcommunity Thisthesisisaboutapplicationsofmachine learning methods to study biological phenomena from a complex systems viewpoint
To decipher the complexity of cancer an integrated approach is required providing a comprehensive and mechanistic overview of the involved processes
The 17th conference on Computational Methods in Systems Biology CMSB 2019 will take place between the 18th to 20th September 2018 in Trieste Italy Its aim is to bring together researchers from across biological mathematical computational and physical sciences who are interested in the study modelling simulation advanced analysis and design of biological systems
Thomas Walter Centre for Computational Biology MINES ParisTech Paris France JeanPhilippe Vert Google MINES ParisTech Paris France Antoine De Weck Novartis Basel Switzerland
This raises computational and statistical challenges which are precisely the focus of study of the machine learning community This thesis is about applications of machine learning methods to study biological phenomena from a complex systems viewpoint We apply machine learning methods in the context of proteinligand interaction and side effect analysis cell population phenotyping and