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Big data analytics in genomics (4회 대출)

자료유형

단행본

개인저자

Wong, Ka-Chun.

서명 / 저자사항

Big data analytics in genomics / [ed. by] Ka-Chun Wong.

발행사항

[Cham] : Springer, 2016.

형태사항

viii, 428 p. : ill. ; 25 cm.

기타형태 저록

Online version: Wong, Ka-Chun. Big data analytics in genomics Cham : Springer, 2016. 9783319412795 (211009) 000046029851

ISBN

9783319412788

일반주기

Online version: Wong, Ka-Chun. Big data analytics in genomics Cham : Springer, 2016. 9783319412795

서지주기

Includes bibliographical references.

일반주제명

Genomics --statistics & numerical data. Datasets as Topic. Statistics as Topic. Neoplasms --genetics.

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090		▼a 005.7 ▼b B592
245	0 0	▼a Big data analytics in genomics / ▼c [ed. by] Ka-Chun Wong.
260		▼a [Cham] : ▼b Springer, ▼c 2016.
300		▼a viii, 428 p. : ▼b ill. ; ▼c 25 cm.
504		▼a Includes bibliographical references.
650	0	▼a Genomics ▼x statistics & numerical data.
650	0	▼a Datasets as Topic.
650	0	▼a Statistics as Topic.
650	0	▼a Neoplasms ▼x genetics.
700	1	▼a Wong, Ka-Chun.
776	0 8	▼i Online version: ▼a Wong, Ka-Chun. ▼t Big data analytics in genomics ▼d Cham : Springer, 2016. ▼z 9783319412795 ▼w (211009) 000046029851
945		▼a KLPA

소장정보

과학도서관

No.	소장처	청구기호	등록번호	도서상태	반납예정일	예약	서비스
No. 1	소장처 과학도서관/Sci-Info(2층서고)/	청구기호 005.7 B592	등록번호 121239844 (4회 대출)	도서상태 대출가능	반납예정일	예약	서비스 B M

컨텐츠정보

책소개

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.

정보제공 :

펼치기

Introduction to Statistical Methods for Integrative Analysis of Genomic Data.- Robust Methods for Expression Quantitative Trait Loci Mapping.- Causal Inference and Structure Learning of Genotype-Phenotype Networks using Genetic Variation.- Genomic Applications of the Neyman-Pearson Classification Paradigm.- Improving Re-annotation of Annotated Eukaryotic Genomes.- State-of-the-art in Smith-Waterman Protein Database Search.- A Survey of Computational Methods for Protein Function Prediction.- Genome Wide Mapping of Nucleosome Position and Histone Code Polymorphisms in Yeast.- Perspectives of Machine Learning Techniques in Big Data Mining of Cancer.- Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms.- NGC Analysis of Somatic Mutations in Cancer Genomes.- OncoMiner: A Pipeline for Bioinformatics Analysis of Exonic Sequence Variants in Cancer.- A Bioinformatics Approach for Understanding Genotype-Phenotype Correlation in Breast Cancer.

정보제공 :

펼치기

Big data analytics in genomics (4회 대출)

소장정보

컨텐츠정보

책소개

목차

청구기호 브라우징

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