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Princeton University Library Catalog
Computational biology and machine learning for metabolic engineering and synthetic biology / edited by Kumar Selvarajoo.
New York, NY : Humana Press, 
xii, 455 pages : illustrations (some color) ; 26 cm.
Methods in molecular biology (Clifton, N.J.) ; v. 2553.
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Springer protocols (Series)
[More in this series]
Methods in molecular biology, 1064-3745 ; 2553
This volume provides protocols for computational, statistical, and machine learning methods that are mainly applied to the study of metabolic engineering, synthetic biology, and disease applications. These techniques support the latest progress in cross-disciplinary research that integrates the different scales of biological complexity. The topics covered in this book are geared toward researchers with a background in engineering, computational analytical, and modeling experience and cover a broad range of topics in computational and machine learning approaches. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Comprehensive and practical, Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology is a valuable resource for any researcher or scientist who wants to learn more about the latest computational methods and how they are applied toward the understanding and prediction of complex biology. .
Includes bibliographical references and index.
Rights and reproductions note
Current copyright fee: GBP19.00 42\0.
Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications
Synthetic Biology Meets Machine Learning
Design and Analysis of Massively Parallel Reporter Assays using FORECAST
Modelling Protein Complexes and Molecular Assemblies using Computational Method
From Genome Mining to Protein Engineering: A Structural Bioinformatics Route
Creating De Novo Overlapped Genes
Design of Gene Boolean Gates and Circuits with Convergent Promoters
Computational Methods for the Design of Recombinase Logic Circuits with Adaptable Circuit Specifications
Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization
Modeling Subcellular Protein Recruitment Dynamics for Synthetic Biology
Genome-Scale Modeling and Systems Metabolic Engineering of Vibrio Natriegens for the Production of 1,3-Propanediol
Application of GeneCloudOmics: Transcriptomics Data Analytics for Synthetic Biology
Overview of Bioinformatics Software and Databases for Metabolic Engineering
Computational Simulation of Tumor-Induced Angiogenesis
Computational Methods and Deep Learning for Elucidating Protein Interaction Networks
Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer
Machine Learning Using Neural Networks for Metabolomic Pathway Analyses
Machine Learning and Hybrid Methods for Metabolic Pathway Modeling
A Machine Learning Based Approach Using Multi Omics Data to Predict Metabolic Pathways.
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Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology [electronic resource] / edited by Kumar Selvarajoo.