Systems Biology And Machine Learning Methods In Reproductive Health
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Systems Biology and Machine Learning Methods in Reproductive Health
Systems Biology and Machine Learning Methods in Reproductive Health is an innovative and wide-ranging book that discovers the synergetic combination of disciplines: systems biology and machine learning, with an application in the field of reproductive health. This book assembles the expertise of leading scientists and clinicians to present a compilation of cutting-edge techniques and case studies utilizing computational methods to elucidate intricate biological systems, elucidate reproductive pathways, and address critical issues in the fields of fertility, pregnancy, and reproductive disorders. Bringing science and data science together, this groundbreaking book provides scientists, clinicians, and students with a step-by-step guide to uncovering the complexities of reproductive health through cutting-edge computational tools.
Data-Driven Reproductive Health
This book provides insight into the transformative impact of data-driven approaches on reproductive health. Chapters cover a wealth of intricate algorithms of genomic analysis, predictive modeling, and personalized treatment strategies, providing an up-to-date view of the reproductive healthcare landscape. With more than 20 code-based examples, the book decodes complex biological data using bioinformatics and machine learning and provides valuable insights into fertility, genetic disorders, and personalized medicine. This book is relevant for healthcare professionals, researchers, and students in the fields of reproductive medicine, bioinformatics, and genetics.
Principles of Computational Genomics
The advent of high-throughput experimental assays, and in particular of next-generation sequencing, has revolutionized life sciences by enabling the generation of data at the scale of the whole genome. Extracting biologically useful or clinically actionable information from this data requires analytical methods quite different from the ones used to analyze low-throughput experimental results. The development of these methods is the goal of computational biology. Understanding the principles on which these methods are based is thus necessary for all students and researchers in life sciences. This book provides the conceptual framework needed to understand computational genomics enough to be able to follow the arguments in recent papers, or to collaborate with computational scientists in research projects. In particular, it introduces the mathematical and statistical basis of the methods in some depth. The main focus is on the analysis of next-generation-sequencing assays, both for the interpretation of the DNA sequence per se (sequence alignment, phylogenetic tree reconstruction, genetic variants) and for the study of gene regulation and epigenomics (gene expression, transcription factor binding, chromatin states, 3D structure of the genome). The final chapter discusses the associations of genetic variants with phenotypes and diseases, and their biological interpretation. Principles of Computational Genomics provides a solid foundation for understanding the many parts of computational genomics, including those not treated directly in the book. It will be of great benefit to students and researchers across the life sciences.