2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) PDF

Author: IEEE Staff

Publisher:

Published: 2013-11-17

Total Pages:

ISBN-13: 9781479934638

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The 2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 13) will be held in Houston, TX during November 17 19, 2013 GENSIPS 13 will provide a forum for signal processing researchers, bioinformaticians, computational biologists, biomedical engineers, and biostatisticians to exchange ideas and discuss the challenges confronting computational bioinformatics and systems biology communities due to the high modality of disparate high throughput data, high variability of data acquisition, high dimensionality of biomedical data, and high complexity of genomics and proteomics

Transcriptomics and Gene Regulation

Transcriptomics and Gene Regulation PDF

Author: Jiaqian Wu

Publisher: Springer

Published: 2015-11-17

Total Pages: 185

ISBN-13: 9401774501

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This volume focuses on modern computational and statistical tools for translational gene expression and regulation research to improve prognosis, diagnostics, prediction of severity, and therapies for human diseases. It introduces some of state of the art technologies as well as computational and statistical tools for translational bioinformatics in the areas of gene transcription and regulation, including the tools for next generation sequencing analyses, alternative spicing, the modeling of signaling pathways, network analyses in predicting disease genes, as well as protein and gene expression data integration in complex human diseases etc. The book is particularly useful for researchers and students in the field of molecular biology, clinical biology and bioinformatics, as well as physicians etc. Dr. Jiaqian Wu is assistant professor in the Vivian L. Smith Department of Neurosurgery and Center for Stem Cell and Regenerative Medicine, University of Texas Health Science Centre, Houston, TX, USA.​

Bioinformatics and Biomedical Engineering

Bioinformatics and Biomedical Engineering PDF

Author: Francisco Ortuño

Publisher: Springer

Published: 2016-03-31

Total Pages: 818

ISBN-13: 331931744X

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This book constitutes the refereed proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016, held in Granada, Spain, in April 2016. The 69 papers presented were carefully reviewed and selected from 286 submissions. The scope of the conference spans the following areas: bioinformatics for healthcare and diseases; biomedical image analysis; biomedical signal analysis; computational systems for modeling biological processes; eHealth; tools for next generation sequencing data analysis; assistive technology for people with neuromotor disorders; fundamentals of biological dynamics and maximization of the information extraction from the experiments in the biological systems; high performance computing in bioinformatics, computational biology and computational chemistry; human behavior monitoring, analysis and understanding; pattern recognition and machine learning in the -omics sciences; and resources for bioinformatics.

Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics

Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics PDF

Author: Benjamin Haibe-Kains

Publisher: Frontiers Media SA

Published: 2015-04-14

Total Pages: 192

ISBN-13: 2889194787

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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.

Fuzzy Systems and Data Mining V

Fuzzy Systems and Data Mining V PDF

Author: A.J. Tallón-Ballesteros

Publisher: IOS Press

Published: 2019-11-06

Total Pages: 1186

ISBN-13: 1643680196

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The Fuzzy Systems and Data Mining (FSDM) conference is an annual event encompassing four main themes: fuzzy theory, algorithms and systems, which includes topics like stability, foundations and control; fuzzy application, which covers different kinds of processing as well as hardware and architectures for big data and time series and has wide applicability; the interdisciplinary field of fuzzy logic and data mining, encompassing applications in electrical, industrial, chemical and engineering fields as well as management and environmental issues; and data mining, outlining new approaches to big data, massive data, scalable, parallel and distributed algorithms. The annual conference provides a platform for knowledge exchange between international experts, researchers, academics and delegates from industry. This book includes the papers accepted and presented at the 5th International Conference on Fuzzy Systems and Data Mining (FSDM 2019), held in Kitakyushu, Japan on 18-21 October 2019. This year, FSDM received 442 submissions. All papers were carefully reviewed by program committee members, taking account of the quality, novelty, soundness, breadth and depth of the research topics falling within the scope of FSDM. The committee finally decided to accept 137 papers, which represents an acceptance rate of about 30%. The papers presented here are arranged in two sections: Fuzzy Sets and Data Mining, and Communications and Networks. Providing an overview of the most recent scientific and technological advances in the fields of fuzzy systems and data mining, the book will be of interest to all those working in these fields.

Error Estimation for Pattern Recognition

Error Estimation for Pattern Recognition PDF

Author: Ulisses M. Braga Neto

Publisher: John Wiley & Sons

Published: 2015-07-07

Total Pages: 336

ISBN-13: 1118999738

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This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification. Error Estimation for Pattern Recognition focuses on error estimation, which is a broad and poorly understood topic that reaches all research areas using pattern classification. It includes model-based approaches and discussions of newer error estimators such as bolstered and Bayesian estimators. This book was motivated by the application of pattern recognition to high-throughput data with limited replicates, which is a basic problem now appearing in many areas. The first two chapters cover basic issues in classification error estimation, such as definitions, test-set error estimation, and training-set error estimation. The remaining chapters in this book cover results on the performance and representation of training-set error estimators for various pattern classifiers. Additional features of the book include: • The latest results on the accuracy of error estimation • Performance analysis of re-substitution, cross-validation, and bootstrap error estimators using analytical and simulation approaches • Highly interactive computer-based exercises and end-of-chapter problems This is the first book exclusively about error estimation for pattern recognition. Ulisses M. Braga Neto is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, USA. He received his PhD in Electrical and Computer Engineering from The Johns Hopkins University. Dr. Braga Neto received an NSF CAREER Award for his work on error estimation for pattern recognition with applications in genomic signal processing. He is an IEEE Senior Member. Edward R. Dougherty is a Distinguished Professor, Robert F. Kennedy ’26 Chair, and Scientific Director at the Center for Bioinformatics and Genomic Systems Engineering at Texas A&M University, USA. He is a fellow of both the IEEE and SPIE, and he has received the SPIE Presidents Award. Dr. Dougherty has authored several books including Epistemology of the Cell: A Systems Perspective on Biological Knowledge and Random Processes for Image and Signal Processing (Wiley-IEEE Press).

Biologically-Inspired Energy Harvesting through Wireless Sensor Technologies

Biologically-Inspired Energy Harvesting through Wireless Sensor Technologies PDF

Author: Ponnusamy, Vasaki

Publisher: IGI Global

Published: 2016-04-05

Total Pages: 318

ISBN-13: 1466697938

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The need for sustainable sources of energy has become more prevalent in an effort to conserve natural resources, as well as optimize the performance of wireless networks in daily life. Renewable sources of energy also help to cut costs while still providing a reliable power sources. Biologically-Inspired Energy Harvesting through Wireless Sensor Technologies highlights emerging research in the areas of sustainable energy management and transmission technologies. Featuring technological advancements in green technology, energy harvesting, sustainability, networking, and autonomic computing, as well as bio-inspired algorithms and solutions utilized in energy management, this publication is an essential reference source for researchers, academicians, and students interested in renewable or sustained energy in wireless networks.

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