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| 008 | 160615s2016 gw | s |||| 0|eng d | ||
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_aQH324.25 _bF453 2016 EB |
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| 100 | 1 |
_aFeldbauer, Roman _9100108 _0Local |
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_aMachine Learning for Microbial Phenotype Prediction _cby Roman Feldbauer |
| 260 |
_aWiesbaden _bSpringer Fachmedien Wiesbaden Spektrum _c2016 |
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| 300 |
_a1 recurso en línea (XIII, 110 p.) _b29 ilustraciones |
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| 336 |
_aTexto (visual) _btxt _2rdacontent |
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_aelectrónico _bc _2rdamedia |
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_arecurso electrónico _bcr _2rdacarrier |
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| 490 | 1 | _aBestMasters | |
| 505 | 0 | _aMicrobial Genotypes and Phenotypes -- Basics of Machine Learning -- Phenotype Prediction Packages -- A Model for Intracellular Lifestyle. | |
| 520 | 3 | _aThis 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 | |
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_aBioinformática _0comprobar BNE20022028248 _2embne _9160489 |
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_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-658-14319-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
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_a.b12956958 _b10-10-17 _c21-11-16 |
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