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 ground-breaking book provides scientists, clinicians, and students with a step-by-step guide to uncovering the complexities of reproductive health through cutting-edge computational tools.
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.
Reproductive health and rights are critical topics in today’s society as laws and policies are continuously debated and adjusted across the world. There are many different outlooks on these issues, and different countries have widely varying laws in place at present. In order to better understand where the world currently is regarding these pressing discussions, further study is needed on the status of women’s reproductive rights. The Research Anthology on Advancements in Women's Health and Reproductive Rights provides a thorough review of the current research available regarding reproductive health. The book discusses how various countries and regions are handling reproductive rights as well as current issues women face within their reproductive health journeys. Covering topics such as sexual health, gender, and pregnancy, this major reference work is ideal for nurses, government officials, policymakers, healthcare professionals, researchers, scholars, academicians, practitioners, instructors, and students.
This work presents the latest development in the field of computational intelligence to advance Big Data and Cloud Computing concerning applications in medical diagnosis. As forum for academia and professionals it covers state-of-the-art research challenges and issues in the digital information & knowledge management and the concerns along with the solutions adopted in these fields.
This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine by combining design and problem-solving skills of engineering with health sciences to advance healthcare treatment. Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques discusses AI-based smart paradigms for reliable prediction of infectious disease dynamics; such paradigms can help prevent disease transmission. It highlights the different aspects of using extended reality for diverse healthcare applications and aggregates the current state of research. The book offers intelligent models of the smart recommender system for personal well-being services and computer-aided drug discovery and design methods. Case studies illustrating the business processes that underlie the use of big data and health analytics to improve healthcare delivery are center stage. Innovative techniques used for extracting user social behavior (known as sentiment analysis for healthcare-related purposes) round out the diverse array of topics this reference book covers. Contributions from experts in the field, this book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering.
Artificial intelligence models are being used to make labor and delivery safer for mothers and newborns. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. This is a critical area of study as maternal and infant health are indispensable for a healthy society. Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning considers the recent advances, challenges, and best practices of artificial intelligence and machine learning in relation to pregnancy complications. Covering key topics such as pregnancy complications, wearable sensors, and healthcare technologies, this premier reference source is ideal for nurses, doctors, computer scientists, medical professionals, industry professionals, researchers, academicians, scholars, instructors, and students.
We are publishing this book as the result of a research project carried out by the University of Las Palmas de Gran Canaria in Spain and AFM Krakow University in Poland. Some parts of it were already announced during a scientific Conference organised remotely in Kraków in October 2020. It is now time to present the research findings in writing.The issue of Artificial Intelligence has long raised questions and interests, including those of legal science. A number of problems have not yet been widely analysed, despite the fact that the present time is undoubtedly a time of technological challenges. Therefore, in the presented publication, prepared by the international scientific community, under our scientific guidance, the authors try to analyse the areas which, in their opinion and in our opinion, required such analysis. The leitmotif of our scientific work was human rights and their relationship with Artificial Intelligence. In presenting the research results in this book, we realise that a number of issues still need to be clarified. Nevertheless, we hope that the work presented for the reader’s consideration will constitute an interesting voice in the discussion, a point of reference for all those dealing with the legal issues of new technologies and the protection of human rights.As the presented book is a collective work, the authors essentially present their own views. The whole work has been designed to address a broad spectrum of issues in Artificial Intelligence and human rights in a single collection. We are convinced that such a broad view will allow everyone interested in the discussed issues to see the essence of contemporary problems faced by the science of law in a multinational perspective.We encourage everyone to read the book!Laura Miraut MartínMariusz Zalucki
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
