{"product_id":"machine-learning-for-microbial-phenotype-prediction-9783658143183","title":"Machine Learning for Microbial Phenotype Prediction","description":"\u003cp\u003e • Author(s): Roman Feldbauer\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer Spektrum\u003cbr\u003e • BISAC: Life Sciences - Anatomy \u0026amp; Physiology\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom the Back Cover\u003c\/b\u003e\u003cbr\u003eThis 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. \u003cbr\u003e\u003cb\u003eContents\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eMicrobial Genotypes and Phenotypes\u003c\/li\u003e\n\u003cli\u003eBasics of Machine Learning\u003c\/li\u003e\n\u003cli\u003ePhenotype Prediction Packages\u003c\/li\u003e\n\u003cli\u003eA Model for Intracellular Lifestyle\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eTarget Groups \u003c\/b\u003e\u003cli\u003eTeachers and students in the fields of bioinformatics, molecular biology and microbiology\u003c\/li\u003e\u003cli\u003eExecutives and specialists in the field of microbiology, computational biology and machine learning\u003c\/li\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cb\u003eRoman Feldbauer\u003c\/b\u003e 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.\" \u003cbr\u003e","brand":"Springer","offers":[{"title":"Paperback","offer_id":45279968723095,"sku":"9783658143183","price":3639.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9783658143183.webp?v=1769295031","url":"https:\/\/atlanticbooks.com\/products\/machine-learning-for-microbial-phenotype-prediction-9783658143183","provider":"Atlantic Books","version":"1.0","type":"link"}