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Beschreibung
The book discusses how Machine Learning and Big Data is and can be used in biotechnology for a wide breath of topics. It is separated
into three main parts, with the first covering DNA and ranging from ?synthetic biology part design (such as promoters)? to ?predictions from genome sequences?. The second part concerns proteins, with topics ranging from ?structure and design tools? to ?pathway discovery / retrobiosynthesis?, while the last part covers whole cells and ranges from ?Machine Learning approaches for gene expression? to ?Machine Learning predictions of phenotype and bioreactor performance?
The book discusses how Machine Learning and Big Data is and can be used in biotechnology for a wide breath of topics. It is separated
into three main parts, with the first covering DNA and ranging from ?synthetic biology part design (such as promoters)? to ?predictions from genome sequences?. The second part concerns proteins, with topics ranging from ?structure and design tools? to ?pathway discovery / retrobiosynthesis?, while the last part covers whole cells and ranges from ?Machine Learning approaches for gene expression? to ?Machine Learning predictions of phenotype and bioreactor performance?
Über den Autor

Dr. Hal S. Alper 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.

Inhaltsverzeichnis
Part I - From DNA?
1 Deep learning approaches for synthetic biology part design
2 Automated approaches for GSM development from DNA sequence
3 Predictive models from genome sequences
Part II - ?.to Proteins?
4 De novo protein structure and design tools
5 Machine learning approaches for protein engineering
6 Pathway discovery / Retrobiosynthesis
7 Enzyme functional classifications
8 Proteomics machine learning approaches and de novo identification
Part III - ?to whole cells and beyond
9 Machine learning approaches for gene expression
10 Metabolomics big data approaches
11 Use of Generative AI and natural language processing for cell models
12 Metabolic production, strain engineering, and flux design
13 Automated function and learning in biofoundries/strain designs
14 Machine learning predictions of phenotype and bioreactor performance
Details
Erscheinungsjahr: 2026
Fachbereich: Populäre Darstellungen
Genre: Chemie, Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: 432 S.
19 s/w Tab.
19 Illustr.
ISBN-13: 9783527354740
ISBN-10: 3527354743
Sprache: Englisch
Herstellernummer: 1135474 000
Einband: Gebunden
Redaktion: Alper, Hal S.
Herausgeber: Hal S Alper
Hersteller: Wiley-VCH GmbH
Verantwortliche Person für die EU: Wiley-VCH GmbH, Boschstr. 12, D-69469 Weinheim, product-safety@wiley.com
Abbildungen: 19 schwarz-weiße Tabellen
Maße: 247 x 174 x 28 mm
Von/Mit: Hal S. Alper
Erscheinungsdatum: 04.03.2026
Gewicht: 0,958 kg
Artikel-ID: 134717429

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