Machine Learning for Microbial Phenotype Prediction
This thesis presents a scalable, generic methodology for microbial phenotype prediction based on supervised machine learning, several models for biological and ecological traits of high relevance, and the deployment in metagenomic datasets. The results suggest that the presented prediction tool can...
| Autor principal: | |
|---|---|
| Autor Corporativo: | |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Wiesbaden :
Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum
2016.
|
| Colección: | BestMasters.
Springer eBooks. |
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b34537272*spi |
| Sumario: | This thesis presents a scalable, generic methodology for microbial phenotype prediction based on supervised machine learning, several models for biological and ecological traits of high relevance, and the deployment in metagenomic datasets. The results suggest that the presented prediction tool can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomic studies. Unraveling relationships between a living organism's genetic information and its observable traits is a central biological problem. Phenotype prediction facilitated by machine learning techniques will be a major step forward to creating biological knowledge from big data. Contents Microbial Genotypes and Phenotypes Basics of Machine Learning Phenotype Prediction Packages A Model for Intracellular Lifestyle Target Groups Teachers and students in the fields of bioinformatics, molecular biology and microbiology Executives and specialists in the field of microbiology, computational biology and machine learning About the Author Roman Feldbauer is currently employed at the Austrian Research Institute for Artificial Intelligence (OFAI) and PhD student at the University of Vienna. His research interests are machine learning, data science, bioinformatics, comparative genomics and neuroscience. In one of his current projects he investigates large biological databases in regard to the „curse of dimensionalityz. . |
|---|---|
| Descripción Física: | XIII, 110 p. 29 il |
| Formato: | Forma de acceso: World Wide Web. |
| ISBN: | 9783658143190 |
