Machine Learning and Big Data-enabled Biotechnology

Machine Learning and Big Data-enabled Biotechnology, 1st Edition, by Hal S. Alper, is a textbook written to guide reaction, process, catalytic, and environmental engineers, as well as biological researchers, in applying computational methods to biological design. The text establishes quantitative frameworks intended to support molecular engineering and biological system development.

The material presents deep learning approaches focused on synthetic biology part design alongside automated techniques for developing genome-scale models from DNA sequences. Additional sections examine tools for de novo protein structure prediction and design, detailing how analytical platforms assist pathway discovery, retrobiosynthetic mapping, and enzyme functional classifications.

By connecting molecular sequence analysis with systematic enzyme categorization, this volume provides structured instruction for technical specialists and academic researchers building predictive biological models.