Machine Learning for Microbial Phenotype Prediction

Machine Learning for Microbial Phenotype Prediction
Springer | Bioinformatics | Jul 17 2016 | ISBN-10: 3658143185 | 105 pages | pdf | 3.54 mb

Authors: Feldbauer, Roman
Publication in the field of Bioinformatic Science
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.

Number of Illustrations and Tables
29 b/w illustrations
Mathematical and Computational Biology

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