2010 Ieee International Workshop On Genomic Signal Processing And Statistics
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| Author | : Matthias Dehmer |
| Publisher | : John Wiley & Sons |
| Total Pages | : 301 |
| Release | : 2012-11-28 |
| Genre | : Medical |
| ISBN | : 3527665455 |
This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.
| Author | : Pey-Chang Kent Lin |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 112 |
| Release | : 2013-10-31 |
| Genre | : Technology & Engineering |
| ISBN | : 146149429X |
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.
| Author | : Benjamin Haibe-Kains |
| Publisher | : Frontiers Media SA |
| Total Pages | : 192 |
| Release | : 2015-04-14 |
| Genre | : Bioengineering |
| ISBN | : 2889194787 |
Scientists today have access to an unprecedented arsenal of high-tech tools that can be used to thoroughly characterize biological systems of interest. High-throughput “omics” technologies enable to generate enormous quantities of data at the DNA, RNA, epigenetic and proteomic levels. One of the major challenges of the post-genomic era is to extract functional information by integrating such heterogeneous high-throughput genomic data. This is not a trivial task as we are increasingly coming to understand that it is not individual genes, but rather biological pathways and networks that drive an organism’s response to environmental factors and the development of its particular phenotype. In order to fully understand the way in which these networks interact (or fail to do so) in specific states (disease for instance), we must learn both, the structure of the underlying networks and the rules that govern their behavior. In recent years there has been an increasing interest in methods that aim to infer biological networks. These methods enable the opportunity for better understanding the interactions between genomic features and the overall structure and behavior of the underlying networks. So far, such network models have been mainly used to identify and validate new interactions between genes of interest. But ultimately, one could use these networks to predict large-scale effects of perturbations, such as treatment by multiple targeted drugs. However, currently, we are still at an early stage of comprehending methods and approaches providing a robust statistical framework to quantitatively assess the quality of network inference and its predictive potential. The scope of this Research Topic in Bioinformatics and Computational Biology aims at addressing these issues by investigating the various, complementary approaches to quantify the quality of network models. These “validation” techniques could focus on assessing quality of specific interactions, global and local structures, and predictive ability of network models. These methods could rely exclusively on in silico evaluation procedures or they could be coupled with novel experimental designs to generate the biological data necessary to properly validate inferred networks.
| Author | : Md. Zia Ur Rahman |
| Publisher | : CRC Press |
| Total Pages | : 202 |
| Release | : 2021-06-30 |
| Genre | : Science |
| ISBN | : 1000375153 |
This book addresses the issue of improving the accuracy in exon prediction in DNA sequences using various adaptive techniques based on different performance measures that are crucial in disease diagnosis and therapy. First, the authors present an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods, followed by a review of literature starting with the biological background of genomic sequence analysis. Next, they cover various theoretical considerations of adaptive filtering techniques used for DNA analysis, with an introduction to adaptive filtering, properties of adaptive algorithms, and the need for development of adaptive exon predictors (AEPs) and structure of AEP used for DNA analysis. Then, they extend the approach of least mean squares (LMS) algorithm and its sign-based realizations with normalization factor for DNA analysis. They also present the normalized logarithmic-based realizations of least mean logarithmic squares (LMLS) and least logarithmic absolute difference (LLAD) adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants. This book ends with an overview of the goals achieved and highlights the primary achievements using all proposed techniques. This book is intended to provide rigorous use of adaptive signal processing algorithms for genetic engineering, biomedical engineering, and bioinformatics and is useful for undergraduate and postgraduate students. This will also serve as a practical guide for Ph.D. students and researchers and will provide a number of research directions for further work. Features Presents an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods Covers various theoretical considerations of adaptive filtering techniques used for DNA analysis, introduction to adaptive filtering, properties of adaptive algorithms, need for development of adaptive exon predictors (AEPs), and structure of AEP used for DNA analysis Extends the approach of LMS algorithm and its sign-based realizations with normalization factor for DNA analysis Presents the normalized logarithmic-based realizations of LMLS and LLAD adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants Provides an overview of the goals achieved and highlights the primary achievements using all proposed techniques Dr. Md. Zia Ur Rahman is a professor in the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His current research interests include adaptive signal processing, biomedical signal processing, genetic engineering, medical imaging, array signal processing, medical telemetry, and nanophotonics. Dr. Srinivasareddy Putluri is currently a Software Engineer at Tata Consultancy Services Ltd., Hyderabad. He received his Ph.D. degree (Genomic Signal Processing using Adaptive Signal Processing algorithms) from the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His research interests include genomic signal processing and adaptive signal processing. He has published 15 research papers in various journals and proceedings. He is currently a reviewer of publishers like the IEEE Access and IGI.
| Author | : Chenxiao Cai |
| Publisher | : Springer |
| Total Pages | : 233 |
| Release | : 2016-09-28 |
| Genre | : Technology & Engineering |
| ISBN | : 3319454056 |
This book is a self-contained collection of recent research findings providing a comprehensive and systematic unified framework for both analysis and synthesis for singularly perturbed systems. It paves the way for the gap between frequency-domain-transfer-function-based results and time-domain-state-space-based results to be bridged. It is divided into three parts focusing on: fundamental background of singular perturbation; general singular perturbation methodologies and time-scale techniques and the theoretical foundation of finite-frequency control; the analysis and synthesis of singularly perturbed systems; and real-world engineering applications implementing the results developed in systems like wind turbines and autonomous-aerial-vehicle hovering. It also presents solutions to analysis and design problems in terms of linear matrix inequalities. Lastly, it provides valuable reference material for researchers who wish to explore the design of controllers for such systems.
| Author | : Eugenio Iannone |
| Publisher | : CRC Press |
| Total Pages | : 1351 |
| Release | : 2018-09-03 |
| Genre | : Medical |
| ISBN | : 1351832069 |
Labs on Chip: Principles, Design and Technology provides a complete reference for the complex field of labs on chip in biotechnology. Merging three main areas— fluid dynamics, monolithic micro- and nanotechnology, and out-of-equilibrium biochemistry—this text integrates coverage of technology issues with strong theoretical explanations of design techniques. Analyzing each subject from basic principles to relevant applications, this book: Describes the biochemical elements required to work on labs on chip Discusses fabrication, microfluidic, and electronic and optical detection techniques Addresses planar technologies, polymer microfabrication, and process scalability to huge volumes Presents a global view of current lab-on-chip research and development Devotes an entire chapter to labs on chip for genetics Summarizing in one source the different technical competencies required, Labs on Chip: Principles, Design and Technology offers valuable guidance for the lab-on-chip design decision-making process, while exploring essential elements of labs on chip useful both to the professional who wants to approach a new field and to the specialist who wants to gain a broader perspective.
| Author | : Valter Tucci |
| Publisher | : John Wiley & Sons |
| Total Pages | : 648 |
| Release | : 2017-03-06 |
| Genre | : Medical |
| ISBN | : 1118540719 |
The Handbook of Behavioral Genetics and Phenotyping represents an integrative approach to neurobehavioural genetics; worldwide experts in their field will review all chapters. Advanced overviews of neurobehavioural characteristics will add immense value to the investigation of animal mutants and provide unique information about the genetics and behavioural understanding of animal models, under both normal and pathological conditions. Cross-species comparisons of neurobehavioural phenotypes will pave the way for an evolutionary understanding of behaviour. Moreover, while biological sciences are progressing towards a holistic approach to investigate the complexity of organisms (i.e., “systems biology” approach), an integrated analysis of behavioural phenotyping is still lacking. The Handbook of Behavioral Genetics and Phenotyping strengthens the cross-talk within disciplines that investigate the fundamental basis of behaviour and genetics. This will be the first volume in which traditionally distant fields including genomics, behaviour, electrophysiology, neuroeconomics, and computational neuroscience, among others, are evaluated together and simultaneously accounted for during discussions of future perspectives.
| Author | : Hitoshi Iba |
| Publisher | : John Wiley & Sons |
| Total Pages | : 464 |
| Release | : 2016-01-20 |
| Genre | : Computers |
| ISBN | : 1119079772 |
Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. • Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) • Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications • Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology • Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
| Author | : Diego A. Forero |
| Publisher | : CRC Press |
| Total Pages | : 509 |
| Release | : 2021-12-23 |
| Genre | : Science |
| ISBN | : 1000405680 |
Advances in high-throughput biological methods have led to the publication of a large number of genome-wide studies in human and animal models. In this context, recent tools from bioinformatics and computational biology have been fundamental for the analysis of these genomic studies. The book Bioinformatics and Human Genomics Research provides updated and comprehensive information about multiple approaches of the application of bioinformatic tools to research in human genomics. It covers strategies analysis of genome-wide association studies, genome-wide expression studies and genome-wide DNA methylation, among other topics. It provides interesting strategies for data mining in human genomics, network analysis, prediction of binding sites for miRNAs and transcription factors, among other themes. Experts from all around the world in bioinformatics and human genomics have contributed chapters in this book. Readers will find this book as quite useful for their in silico explorations, which would contribute to a better and deeper understanding of multiple biological processes and of pathophysiology of many human diseases.
| Author | : Hamid R Arabnia |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 670 |
| Release | : 2015-08-11 |
| Genre | : Computers |
| ISBN | : 0128026464 |
Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology discusses the latest developments in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytics and algorithms, mathematical modeling, and simu- lation techniques. • Discusses the development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to the study of biological and behavioral systems, including applications in cancer research, computational intelligence and drug design, high-performance computing, and biology, as well as cloud and grid computing for the storage and access of big data sets. • Presents a systematic approach for storing, retrieving, organizing, and analyzing biological data using software tools with applications to general principles of DNA/RNA structure, bioinformatics and applications, genomes, protein structure, and modeling and classification, as well as microarray analysis. • Provides a systems biology perspective, including general guidelines and techniques for obtaining, integrating, and analyzing complex data sets from multiple experimental sources using computational tools and software. Topics covered include phenomics, genomics, epigenomics/epigenetics, metabolomics, cell cycle and checkpoint control, and systems biology and vaccination research. • Explains how to effectively harness the power of Big Data tools when data sets are so large and complex that it is difficult to process them using conventional database management systems or traditional data processing applications. - Discusses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological and behavioral systems. - Presents a systematic approach for storing, retrieving, organizing and analyzing biological data using software tools with applications. - Provides a systems biology perspective including general guidelines and techniques for obtaining, integrating and analyzing complex data sets from multiple experimental sources using computational tools and software.
