Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Recent Advances in Biological Network Analysis
This book reviews recent advances in the emerging field of computational network biology with special emphasis on comparative network analysis and network module detection. The chapters in this volume are contributed by leading international researchers in computational network biology and offer in-depth insight on the latest techniques in network alignment, network clustering, and network module detection. Chapters discuss the advantages of the respective techniques and present the current challenges and open problems in the field. Recent Advances in Biological Network Analysis: Comparative Network Analysis and Network Module Detection will serve as a great resource for graduate students, academics, and researchers who are currently working in areas relevant to computational network biology or wish to learn more about the field. Data scientists whose work involves the analysis of graphs, networks, and other types of data with topological structure or relations can also benefit from the book's insights.
Micro Total Analysis Systems 2002
The Sixth International Conference on Miniaturized Chemical and Biochemical Analysis Systems, known as IlTAS2002, will be fully dedicated to the latest scientific and technological developments in the field of miniaturized devices and systems for realizing not only chemical and biochemical analysis but also synthesis. The first IlTAS meeting was held in Enschede in 1994 with approximately 160 participants, bringing together the scientists with background in analytical and biochemistry with those with Micro Electro Mechanical Systems (MEMS) in one workshop. We are grateful to Piet Bergveld and Albert van den Berg of MESA Research Institute of the University of Twente for their great efforts t...
RNA Structure Prediction
This book explores recent progress in RNA secondary, tertiary structure prediction, and its application from an expansive point of view. Because of advancements in experimental protocols and devices, the integration of new types of data as well as new analysis techniques is necessary, and this volume discusses additional topics that are closely related to RNA structure prediction, such as the detection of structure-disrupting mutations, high-throughput structure analysis, and 3D structure design. Written for the highly successful Methods in Molecular Biology series, chapters feature the kind of detailed implementation advice that leads to quality research results. Authoritative and practical, RNA Structure Prediction serves as a valuable guide for both experimental and computational RNA researchers.
Artificial Immune Systems
Arti?cial Immune Systems have come of age. They are no longer an obscure computersciencetechnique,workedonbyacoupleoffarsightedresearchgroups. Today, researchers across the globe are working on new computer algorithms inspired by the workings of the immune system. This vigorous ?eld of research investigates how immunobiology can assist our technology, and along the way is beginning to help biologists understand their unique problems. AIS is now old enough to understand its roots, its context in the research community, and its exciting future. It has grown too big to be con?ned to s- cial sessions in evolutionary computation conferences. AIS researchers are now forming their own community and...
Big Data over Networks
Examines the crucial interaction between big data and communication, social and biological networks using critical mathematical tools and state-of-the-art research.
Methods and Applications of Autonomous Experimentation
Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences, this book is a practical guide to successful Autonomous Experimentation. Despite the field’s growing potential, there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists, machine-learning engineers and applied scientists, this book aims to lay the foundation for future research and widespread adoption within the scientific community. This book is particularly useful for members of the scientific community looking to improve their research methods but also contains additional insights for students and industry professionals interested in the future of the field.
Logic Synthesis for Genetic Diseases
This book brings to bear a body of logic synthesis techniques, in order to contribute to the analysis and control of Boolean Networks (BN) for modeling genetic diseases such as cancer. The authors provide several VLSI logic techniques to model the genetic disease behavior as a BN, with powerful implicit enumeration techniques. Coverage also includes techniques from VLSI testing to control a faulty BN, transforming its behavior to a healthy BN, potentially aiding in efforts to find the best candidates for treatment of genetic diseases.
Signals, Systems and Signal Processing
An innovative introduction to the foundations of signals and systems, smoothing the transition towards study of digital signal processing.
Artificial Intelligence For Science: A Deep Learning Revolution
This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.
