Machine Learning for Microbial Phenotype Prediction

Roman Feldbauer

ISBN 10: 3658143185 / ISBN 13: 9783658143183

Published by Springer Fachmedien Wiesbaden Jun 2016, 2016

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 124 pp. Englisch.

Seller Inventory # 9783658143183

Bibliographic Details

• Title: Machine Learning for Microbial Phenotype Prediction
• Publisher: Springer Fachmedien Wiesbaden Jun 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 3658143185
• ISBN 13: 9783658143183
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 8D millimeters 210 millimeters width by 148 millimeters height by 8 millimeters depth
• Weight: 172 grams

About this title

Synopsis

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.

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“.