@article{755963,
      recid = {755963},
      author = {Feldbauer, Roman,},
      title = {Machine learning for microbial phenotype prediction  [electronic resource] /},
      pages = {1 online resource (xiii, 110 pages) :},
      abstract = {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  dimensionality". .},
      url = {http://library.usi.edu/record/755963},
}