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Machine Learning for Microbial Phenotype Prediction
Roman Feldbauer
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Description for Machine Learning for Microbial Phenotype Prediction
paperback. XYZ Series: BestMasters. Num Pages: 123 pages, 29 black & white illustrations, biography. BIC Classification: PDE; PSG. Category: (G) General (US: Trade). Dimension: 210 x 148 x 7. Weight in Grams: 173.
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.
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.
Product Details
Format
Paperback
Publication date
2016
Publisher
Springer Fachmedien Wiesbaden Germany
Number of pages
123
Condition
New
Series
BestMasters
Number of Pages
110
Place of Publication
Weisbaden, Germany
ISBN
9783658143183
SKU
V9783658143183
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About Roman Feldbauer
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“.
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