Big Data Analytics In Computational Genome Sequence Analysis
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Big Data Analytics In Computational Genome Sequence Analysis
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Author : Dr. F. Amul Mary & Dr. S. Jyothi
language : en
Publisher: Ashok Yakkaldevi
Release Date : 2022-01-18
Big Data Analytics In Computational Genome Sequence Analysis written by Dr. F. Amul Mary & Dr. S. Jyothi and has been published by Ashok Yakkaldevi this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-18 with Art categories.
The genomes in human body programs the blueprint of one’s life but the functions of those genomes nearly three billion genome bases are not known. The genome sequence in human being gives the fundamental rules for human biology. Science makes every effort to reveal the laws of nature and critical understanding of the biology. Scientists in the life-science field are seeking genetic variants associated with multifaceted set of observable characteristics to advance our understanding about genetics. Technological advancements are assisting the scientists to quickly create, store and analyze the data as fast as possible and as efficient as possible. The NCBI and other organizations maintain genome sequences, proteins, RNA, DNA and other information of all species as well as their behavioral data. There is a lot and lot of data. Translating these data into useful insights which can be used for research and innovation is a main concern.
Big Data Analytics In Genomics
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Author : Ka-Chun Wong
language : en
Publisher: Springer
Release Date : 2016-10-24
Big Data Analytics In Genomics written by Ka-Chun Wong and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-24 with Computers categories.
This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.
Computational Intelligence And Big Data Analytics
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Author : Ch. Satyanarayana
language : en
Publisher: Springer
Release Date : 2018-09-22
Computational Intelligence And Big Data Analytics written by Ch. Satyanarayana and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-22 with Computers categories.
This book highlights major issues related to big data analysis using computational intelligence techniques, mostly interdisciplinary in nature. It comprises chapters on computational intelligence technologies, such as neural networks and learning algorithms, evolutionary computation, fuzzy systems and other emerging techniques in data science and big data, ranging from methodologies, theory and algorithms for handling big data, to their applications in bioinformatics and related disciplines. The book describes the latest solutions, scientific results and methods in solving intriguing problems in the fields of big data analytics, intelligent agents and computational intelligence. It reflects the state of the art research in the field and novel applications of new processing techniques in computer science. This book is useful to both doctoral students and researchers from computer science and engineering fields and bioinformatics related domains.
Advances In Genomic Sequence Analysis And Pattern Discovery
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Author : Laura Elnitski
language : en
Publisher: World Scientific
Release Date : 2011
Advances In Genomic Sequence Analysis And Pattern Discovery written by Laura Elnitski and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Science categories.
Mapping the genomic landscapes is one of the most exciting frontiers of science. We have the opportunity to reverse engineer the blueprints and the control systems of living organisms. Computational tools are key enablers in the deciphering process. This book provides an in-depth presentation of some of the important computational biology approaches to genomic sequence analysis. The first section of the book discusses methods for discovering patterns in DNA and RNA. This is followed by the second section that reflects on methods in various ways, including performance, usage and paradigms.
Computational Genomics With R
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Author : Altuna Akalin
language : en
Publisher: CRC Press
Release Date : 2020-12-16
Computational Genomics With R written by Altuna Akalin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-16 with Mathematics categories.
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.
Big Data Analytics In Bioinformatics And Healthcare
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Author : Baoying Wang
language : en
Publisher:
Release Date : 2014-10
Big Data Analytics In Bioinformatics And Healthcare written by Baoying Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10 with Data mining categories.
"This book merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management"--
Introduction To Computational Metagenomics
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Author : Zhong Wang
language : en
Publisher: World Scientific
Release Date : 2022-04-11
Introduction To Computational Metagenomics written by Zhong Wang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-11 with Science categories.
Breakthroughs in high-throughput genome sequencing and high-performance computing technologies have empowered scientists to decode many genomes including our own. Now they have a bigger ambition: to fully understand the vast diversity of microbial communities within us and around us, and to exploit their potential for the improvement of our health and environment. In this new field called metagenomics, microbial genomes are sequenced directly from the habitats without lab cultivation. Computational metagenomics, however, faces both a data challenge that deals with tens of tera-bases of sequences and an algorithmic one that deals with the complexity of thousands of species and their interactions.This interdisciplinary book is essential reading for those who are interested in beginning their own journey in computational metagenomics. It is a prism to look through various intricate computational metagenomics problems and unravel their three distinctive aspects: metagenomics, data engineering, and algorithms. Graduate students and advanced undergraduates from genomics science or computer science fields will find that the concepts explained in this book can serve as stepping stones for more advanced topics, while metagenomics practitioners and researchers from similar disciplines may use it to broaden their knowledge or identify new research targets.
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.
Big Data In Omics And Imaging
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Author : Momiao Xiong
language : en
Publisher: CRC Press
Release Date : 2017-12-01
Big Data In Omics And Imaging written by Momiao Xiong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-01 with Mathematics categories.
Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.
High Performance Computing
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Author : Ponnuswamy Sadayappan
language : en
Publisher: Springer Nature
Release Date : 2020-06-15
High Performance Computing written by Ponnuswamy Sadayappan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-15 with Computers categories.
This book constitutes the refereed proceedings of the 35th International Conference on High Performance Computing, ISC High Performance 2020, held in Frankfurt/Main, Germany, in June 2020.* The 27 revised full papers presented were carefully reviewed and selected from 87 submissions. The papers cover a broad range of topics such as architectures, networks & infrastructure; artificial intelligence and machine learning; data, storage & visualization; emerging technologies; HPC algorithms; HPC applications; performance modeling & measurement; programming models & systems software. *The conference was held virtually due to the COVID-19 pandemic. Chapters "Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) Streaming-Aggregation Hardware Design and Evaluation", "Solving Acoustic Boundary Integral Equations Using High Performance Tile Low-Rank LU Factorization", "Scaling Genomics Data Processing with Memory-Driven Computing to Accelerate Computational Biology", "Footprint-Aware Power Capping for Hybrid Memory Based Systems", and "Pattern-Aware Staging for Hybrid Memory Systems" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.