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Wiley-VCH, Weinheim Machine Learning and Big Data-enabled Biotechnology Cover .. Product #: 978-3-527-35474-0 Regular price: $148.60 $148.60 Auf Lager

Machine Learning and Big Data-enabled Biotechnology

Alper, Hal S. (Herausgeber)

Advanced Biotechnology

Cover

1. Auflage März 2026
432 Seiten, Hardcover
19 Tabellen
Handbuch/Nachschlagewerk

ISBN: 978-3-527-35474-0
Wiley-VCH, Weinheim

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Weitere Versionen

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
Dr. Hal Alper is the Kenneth A. Kobe Professor in Chemical Engineering and Executive Director of the Center for Biomedical Research Support at The University of Texas at Austin. He earned his Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology in 2006 and was a postdoctoral research associate at the Whitehead Institute for Biomedical Research from 2006-2008, and at Shire Human Genetic Therapies from 2007-2008. Dr. Alper also serves on the Graduate Studies Committee for the Cell and Molecular Biology Department and the Biochemistry Department. He is currently the Principal Investigator of the Laboratory for Cellular and Metabolic Engineering at The University of Texas at Austin where his lab focuses on metabolic and cellular engineering in the context of biofuel, biochemical, and biopharmaceutical production in an array of model host organisms. His research focuses on applying and extending the approaches of synthetic biology, systems
biology, and protein engineering.