| 書(shū)目名稱 | Machine Learning for Microbial Phenotype Prediction |
| 編輯 | Roman Feldbauer |
| 視頻video | http://file.papertrans.cn/621/620632/620632.mp4 |
| 概述 | Publication in the field of Bioinformatic Science.Includes supplementary material: |
| 叢書(shū)名稱 | BestMasters |
| 圖書(shū)封面 |  |
| 描述 | 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. |
| 出版日期 | Book 2016 |
| 關(guān)鍵詞 | bioinformatics; microorganisms; metagenomics; genotype; classification |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-658-14319-0 |
| isbn_softcover | 978-3-658-14318-3 |
| isbn_ebook | 978-3-658-14319-0Series ISSN 2625-3577 Series E-ISSN 2625-3615 |
| issn_series | 2625-3577 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wies |
|Archiver|手機(jī)版|小黑屋|
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