Introduction To Genomic Signal Processing With Control
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Introduction To Genomic Signal Processing With Control
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Author : Aniruddha Datta
language : en
Publisher: CRC Press
Release Date : 2018-10-08
Introduction To Genomic Signal Processing With Control written by Aniruddha Datta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Technology & Engineering categories.
Studying large sets of genes and their collective function requires tools that can easily handle huge amounts of information. Recent research indicates that engineering approaches for prediction, signal processing, and control are well suited for studying multivariate interactions. A tutorial guide to the current engineering research in genomics, Introduction to Genomic Signal Processing with Control provides a state-of-the-art account of the use of control theory to obtain intervention strategies for gene regulatory networks. The book builds up the necessary molecular biology background with a basic review of organic chemistry and an introduction of DNA, RNA, and proteins, followed by a description of the processes of transcription and translation and the genetic code that is used to carry out the latter. It discusses control of gene expression, introduces genetic engineering tools such as microarrays and PCR, and covers cell cycle control and tissue renewal in multi-cellular organisms. The authors then delineate how the engineering approaches of classification and clustering are appropriate for carrying out gene-based disease classification. This leads naturally to expression prediction, which in turn leads to genetic regulatory networks. The book concludes with a discussion of control approaches that can be used to alter the behavior of such networks in the hope that this alteration will move the network from a diseased state to a disease-free state. Written by recognized leaders in this emerging field, the book provides the exact amount of molecular biology required to understand the engineering applications. It is a self-contained resource that spans the diverse disciplines of molecular biology and electrical engineering.
Genomic Signal Processing And Statistics
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Author : Edward R. Dougherty
language : en
Publisher: Hindawi Publishing Corporation
Release Date : 2005
Genomic Signal Processing And Statistics written by Edward R. Dougherty and has been published by Hindawi Publishing Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Technology & Engineering categories.
Recent advances in genomic studies have stimulated synergetic research and development in many cross-disciplinary areas. Processing the vast genomic data, especially the recent large-scale microarray gene expression data, to reveal the complex biological functionality, represents enormous challenges to signal processing and statistics. This perspective naturally leads to a new field, genomic signal processing (GSP), which studies the processing of genomic signals by integrating the theory of signal processing and statistics. Written by an international, interdisciplinary team of authors, this invaluable edited volume is accessible to students just entering this emergent field, and to researchers, both in academia and in industry, in the fields of molecular biology, engineering, statistics, and signal processing. The book provides tutorial-level overviews and addresses the specific needs of genomic signal processing students and researchers as a reference book. The book aims to address current genomic challenges by exploiting potential synergies between genomics, signal processing, and statistics, with special emphasis on signal processing and statistical tools for structural and functional understanding of genomic data. The first part of this book provides a brief history of genomic research and a background introduction from both biological and signal-processing/statistical perspectives, so that readers can easily follow the material presented in the rest of the book. In what follows, overviews of state-of-the-art techniques are provided. We start with a chapter on sequence analysis, and follow with chapters on feature selection, classification, and clustering of microarray data. We then discuss the modeling, analysis, and simulation of biological regulatory networks, especially gene regulatory networks based on Boolean and Bayesian approaches. Visualization and compression of gene data, and supercomputer implementation of genomic signal processing systems are also treated. Finally, we discuss systems biology and medical applications of genomic research as well as the future trends in genomic signal processing and statistics research.
Breakthroughs In Software Science And Computational Intelligence
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Author : Wang, Yingxu
language : en
Publisher: IGI Global
Release Date : 2012-03-31
Breakthroughs In Software Science And Computational Intelligence written by Wang, Yingxu and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-31 with Computers categories.
"This book charts the new ground broken by researchers exploring software science as it interacts with computational intelligence"--
Data Analytics In Medicine Concepts Methodologies Tools And Applications
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2019-12-06
Data Analytics In Medicine Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-06 with Medical categories.
Advancements in data science have created opportunities to sort, manage, and analyze large amounts of data more effectively and efficiently. Applying these new technologies to the healthcare industry, which has vast quantities of patient and medical data and is increasingly becoming more data-reliant, is crucial for refining medical practices and patient care. Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications is a vital reference source that examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations. Highlighting a range of topics such as data security and privacy, health informatics, and predictive analytics, this multi-volume book is ideally designed for doctors, hospital administrators, nurses, medical professionals, IT specialists, computer engineers, information technologists, biomedical engineers, data-processing specialists, healthcare practitioners, academicians, and researchers interested in current research on the connections between data analytics in the field of medicine.
Data Analytics In Bioinformatics
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Author : Rabinarayan Satpathy
language : en
Publisher: John Wiley & Sons
Release Date : 2021-01-20
Data Analytics In Bioinformatics written by Rabinarayan Satpathy and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-20 with Computers categories.
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Logic Synthesis For Genetic Diseases
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Author : Pey-Chang Kent Lin
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-10-31
Logic Synthesis For Genetic Diseases written by Pey-Chang Kent Lin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-31 with Technology & Engineering categories.
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.
What Science Knows
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Author : James Franklin
language : en
Publisher: Encounter Books
Release Date : 2009-11-01
What Science Knows written by James Franklin and has been published by Encounter Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-01 with Science categories.
To scientists, the tsunami of relativism, scepticism, and postmodernism that washed through the humanities in the twentieth century was all water off a duck’s back. Science remained committed to objectivity and continued to deliver remarkable discoveries and improvements in technology. In What Science Knows, the Australian philosopher and mathematician James Franklin explains in captivating and straightforward prose how science works its magic. He begins with an account of the nature of evidence, where science imitates but extends commonsense and legal reasoning in basing conclusions solidly on inductive reasoning from facts. After a brief survey of the furniture of the world as science sees it—including causes, laws, dispositions and force fields as well as material things—Franklin describes colorful examples of discoveries in the natural, mathematical, and social sciences and the reasons for believing them. He examines the limits of science, giving special attention both to mysteries that may be solved by science, such as the origin of life, and those that may in principle be beyond the reach of science, such as the meaning of ethics. What Science Knows will appeal to anyone who wants a sound, readable, and well-paced introduction to the intellectual edifice that is science. On the other hand it will not please the enemies of science, whose willful misunderstandings of scientific method and the relation of evidence to conclusions Franklin mercilessly exposes.
Emerging Research In The Analysis And Modeling Of Gene Regulatory Networks
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Author : Ivanov, Ivan V.
language : en
Publisher: IGI Global
Release Date : 2016-06-06
Emerging Research In The Analysis And Modeling Of Gene Regulatory Networks written by Ivanov, Ivan V. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-06 with Medical categories.
While technological advancements have been critical in allowing researchers to obtain more and better quality data about cellular processes and signals, the design and practical application of computational models of genomic regulation continues to be a challenge. Emerging Research in the Analysis and Modeling of Gene Regulatory Networks presents a compilation of recent and emerging research topics addressing the design and use of technology in the study and simulation of genomic regulation. Exploring both theoretical and practical topics, this publication is an essential reference source for students, professionals, and researchers working in the fields of genomics, molecular biology, bioinformatics, and drug development.
Introduction To Genomics
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Author : Arthur M. Lesk
language : en
Publisher: Oxford University Press
Release Date :
Introduction To Genomics written by Arthur M. Lesk and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on with Genomics categories.
Quantitative Assessment And Validation Of Network Inference Methods In Bioinformatics
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Author : Benjamin Haibe-Kains
language : en
Publisher: Frontiers Media SA
Release Date : 2015-04-14
Quantitative Assessment And Validation Of Network Inference Methods In Bioinformatics written by Benjamin Haibe-Kains and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-14 with Bioengineering categories.
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.