{"product_id":"9783527354740","title":"Machine Learning and Big Data-enabled Biotechnology","description":"\u003ch3\u003eAdvanced Biotechnology\u003c\/h3\u003e\u003ch1\u003eMachine Learning and Big Data-enabled Biotechnology\u003c\/h1\u003e\u003ch3\u003eHal S. Alper\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eScience \/ Biotechnology\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cb\u003eEnables researchers and engineers to gain insights into the capabilities of machine learning approaches to power applications in their fields\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eMachine Learning and Big Data-enabled Biotechnology\u003c\/i\u003e discusses how machine learning and big data can be used in biotechnology for a wide breadth of topics, providing tools essential to support efforts in process control, reactor performance evaluation, and research target identification. \u003c\/p\u003e\n\u003cp\u003eTopics explored in \u003ci\u003eMachine Learning and Big Data-enabled Biotechnology\u003c\/i\u003e include: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDeep learning approaches for synthetic biology part design and automated approaches for GSM development from DNA sequences\u003c\/li\u003e \u003cli\u003eDe novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approaches\u003c\/li\u003e \u003cli\u003eMetabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell models\u003c\/li\u003e \u003cli\u003eAutomated function and learning in biofoundries and strain designs\u003c\/li\u003e \u003cli\u003eMachine learning predictions of phenotype and bioreactor performance\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMachine Learning and Big Data-enabled Biotechnology\u003c\/i\u003e earns a well-deserved spot on the bookshelves of reaction, process, catalytic, and environmental engineers seeking to explore the vast opportunities presented by rapidly developing technologies.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e  \u003cp\u003e\u003ci\u003e\u003cb\u003eDr. Hal S. Alper\u003c\/b\u003e is the Cockrell Family Regents Chair in Engineering #1 at The University of Texas at Austin in the McKetta Department of Chemical Engineering. His research focuses on applying and extending the approaches of metabolic engineering, synthetic biology, systems biology, and protein engineering.\u003c\/i\u003e \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e11 May 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley-VCH\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783527354740\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e432\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e24.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44421682430092,"sku":"9783527354740","price":185.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783527354740_e4d19cc5-c540-4c19-b7c2-61094538f801.jpg?v=1780111772","url":"https:\/\/lateknightbooks.com\/products\/9783527354740","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}