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 LifestyleTarget Groups Teachers and students in the fields of bioinformatics, molecular biology and microbiologyExecutives and specialists in the field of microbiology, computational biology and machine learningAbout the AuthorRoman 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 dimensionality".

Autorentext
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 dimensionality.

Inhalt
Microbial Genotypes and Phenotypes.- Basics of Machine Learning.- Phenotype Prediction Packages.- A Model for Intracellular Lifestyle.