图书简介
Computational Biology for Stem Cell Research seamlessly bridges the gap between the worlds of biomedical sciences and in silico computational methods. This book serves as a valuable resource for researchers and students, enabling them to grasp and delve into the intricacies of hematopoietic Stem Cells (HSCs) and mesenchymal Stem Cells (MSCs) through the lens of computational biology. This perspective sheds light on stem cell transplantation, translational research, and unique properties of stem cells like self-renewal and differentiation. In addition to introducing readers to stem cell-focused bioinformatics tools, this resource offers a clear pathway for effortlessly merging in silico methods with traditional in vitro and in vivo approaches. Computational Biology for Stem Cell Research combines science and technology to showcase how computational methods transform stem cell research by reducing costs and enhancing investigations. The chapters uncover various approaches, from machine learning to genome analysis, for studying networks, protein interactions, dynamics, and the preprocessing of large datasets. The book aims to give readers a broad view of the advanced computational tools and methods extensively employed in stem cell research. Additionally, the book emphasizes the ongoing studies and tools yet to be developed for furthering stem cell research.
Section I - In silico Tools and Approaches in Stem Cell Biology 1. Advancement of In Silico Tools in Stem Cell Research 2. Paradigm shift in stem cell research with computational tools, techniques, and databases 3. Stem Cell Informatics: Web-Resources Aiding in Stem Cell Research 4. Stem Cell-Based Informatics Development and Approaches 5. Application of Machine Learning-Based Approaches in Stem Cell Research 6. Stem Cell Therapy in the Era of Machine Learning 7. Computational and Stem Cell Biology: Challenges and Future Perspectives Section II - Application of Genomic and Proteomic Approaches in Stem Cell Research 8. Single Cell Transcriptome Profiling in Unravelling Distinct Molecular Signatures from Cancer Stem Cells 9. The Single-Cell Big Data Analytics: A Game-Changer in Bioscience 10. Unravelling the genomics and proteomics aspects of the stemness phenotype in stem cells 11. Cutting-Edge Proteogenomics Approaches to Analyze Stem Cells at the Therapeutic Level 12. Advances in Regenerative Medicines Based on Mesenchymal Stem Cell Secretome 13. Paradigms of Omics in Bioinformatics for Accelerating Current Trends and Future Prospects of Stem Cell Research 14. Transcriptomic Profiling-Based Identification Biomarkers of Stem Cells 15. Genomic and Transcriptomic Applications in Neural Stem Cell Therapeutics Section III - Stem Cell Network Modeling and Systems Biology 16. Integration of Multi-omic Data to Identify Transcriptional Targets During Human Hematopoietic Stem Cell Differentiation 17. Computational Approaches to Determine Stem Cell Fate 18. Stem Cell Databases and Tools: Challenges and Opportunities for Computational Biology 19. Deciphering the Complexities of Stem Cells Through Network Biology Approaches for their Application in Regenerative Medicine 20. Bioinformatics Approaches to the Understanding of Notch Signaling in the Biology of Stem Cells 21. In Silico Approaches for the Analyses of Developmental Fate of Stem Cells 22. Exploring imaging technologies and computational resources in stem cell research for regenerative medicine: A comprehensive review 23. Computational Approaches for Hematopoietic Stem Cells: Advancing Regenerative Therapeutics 24. Approaches to Construct and Analyze Stem Cells Regulatory Networks Section IV - Computational Approaches for Stem Cell Tissue Engineering 25. Tissue Engineering in Chondral Defect 26. Recent Advances in Computational Modeling: An Appraisal of Stem Cell and Tissue Engineering Research 27. Computational Approaches for Bioengineering of Cornea 28. Cheminformatics, Metabolomics and Stem Cell Tissue Engineering: A Transformative Insight 29. Targeting Cancer Stem Cells and Harnessing of Computational Tools Offer New Strategies for Cancer Therapy 30. Introduction to Machine Learning and its Applications in Stem Cell Research 31. Multiscale Computational and Machine Learning Models for Designing Stem Cell-Based Regenerative Medicine Therapies 32. Computational Analysis of Epithelial Tissue Regeneration
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