東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ author_sort:"ning, kang." ]

切換: 標籤 | MARC模式 | ISBD

Methodologies of multi-omics data in...

Ning, Kang.

FindBook

Google Book

Amazon

博客來

Methodologies of multi-omics data integration and data mining = techniques and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Methodologies of multi-omics data integration and data mining/ edited by Kang Ning.
其他題名:	techniques and applications /
其他作者:	Ning, Kang.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xi, 167 p. :ill., digital ;24 cm.
內容註:	Chapter 1. Introduction to multi-omics -- Part 1. Omics integration techniques -- Chapter 2. Biomedical applications: the need for multi-omics -- Chapter 3. Omics technologies and big data -- Chapter 4. Multi-omics data mining techniques: algorithms and software -- Part 2. Applications of multi-omics analyses -- Chapter 5. Multi-omics data analysis for cancer research: colorectal cancer, liver cancer and lung cancer -- Chapter 6. Multi-omics data analysis for inflammation disease research: correlation analysis, causal analysis and network analysis -- Chapter 7. Microbiome data analysis and interpretation: correlation inferences and dynamic pattern discovery -- Chapter 8. Current progress of bioinformatics for human health.
Contained By:	Springer Nature eBook
標題:	Data integration (Computer science) - Methodology. -
電子資源:	https://doi.org/10.1007/978-981-19-8210-1
ISBN:	9789811982101

Methodologies of multi-omics data integration and data mining = techniques and applications /
Methodologies of multi-omics data integration and data miningtechniques and applications /[electronic resource] :edited by Kang Ning. - Singapore :Springer Nature Singapore :2023. - xi, 167 p. :ill., digital ;24 cm. - Translational bioinformatics,v. 192213-2783 ;. - Translational bioinformatics ;v. 19..

Chapter 1. Introduction to multi-omics -- Part 1. Omics integration techniques -- Chapter 2. Biomedical applications: the need for multi-omics -- Chapter 3. Omics technologies and big data -- Chapter 4. Multi-omics data mining techniques: algorithms and software -- Part 2. Applications of multi-omics analyses -- Chapter 5. Multi-omics data analysis for cancer research: colorectal cancer, liver cancer and lung cancer -- Chapter 6. Multi-omics data analysis for inflammation disease research: correlation analysis, causal analysis and network analysis -- Chapter 7. Microbiome data analysis and interpretation: correlation inferences and dynamic pattern discovery -- Chapter 8. Current progress of bioinformatics for human health.

This book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the "What", "Why" and "How" of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches.

ISBN: 9789811982101

Standard No.: 10.1007/978-981-19-8210-1doiSubjects--Topical Terms:

3627654
Data integration (Computer science)
--Methodology.

LC Class. No.: QA76.9.D338

Dewey Class. No.: 005.73

Methodologies of multi-omics data integration and data mining = techniques and applications /
LDR:03025nmm a2200337 a 4500 001 2315352
003 DE-He213
005 20230115183619.0
006 m d
007 cr nn 008maaau
008 230902s2023 si s 0 eng d
020 $a 9789811982101 $q (electronic bk.)
020 $a 9789811982095 $q (paper)
024 7 $a 10.1007/978-981-19-8210-1 $2 doi
035 $a 978-981-19-8210-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D338
072 7 $a MBGR $2 bicssc
072 7 $a MED000000 $2 bisacsh
072 7 $a MBGR $2 thema
082 0 4 $a 005.73 $2 23
090 $a QA76.9.D338 $b M592 2023
245 0 0 $a Methodologies of multi-omics data integration and data mining $h [electronic resource] : $b techniques and applications / $c edited by Kang Ning.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xi, 167 p. : $b ill., digital ; $c 24 cm.
490 1 $a Translational bioinformatics, $x 2213-2783 ; $v v. 19
505 0 $a Chapter 1. Introduction to multi-omics -- Part 1. Omics integration techniques -- Chapter 2. Biomedical applications: the need for multi-omics -- Chapter 3. Omics technologies and big data -- Chapter 4. Multi-omics data mining techniques: algorithms and software -- Part 2. Applications of multi-omics analyses -- Chapter 5. Multi-omics data analysis for cancer research: colorectal cancer, liver cancer and lung cancer -- Chapter 6. Multi-omics data analysis for inflammation disease research: correlation analysis, causal analysis and network analysis -- Chapter 7. Microbiome data analysis and interpretation: correlation inferences and dynamic pattern discovery -- Chapter 8. Current progress of bioinformatics for human health.
520 $a This book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the "What", "Why" and "How" of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches.
650 0 $a Data integration (Computer science) $x Methodology. $3 3627654
650 0 $a Data mining $x Methodology. $3 3627655
650 1 4 $a Biomedical Research. $3 851895
650 2 4 $a Bioinformatics. $3 553671
650 2 4 $a Health Informatics. $3 892928
650 2 4 $a Data Analysis and Big Data. $3 3538537
700 1 $a Ning, Kang. $3 3605360
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Translational bioinformatics ; $v v. 19. $3 3627653
856 4 0 $u https://doi.org/10.1007/978-981-19-8210-1
950 $a Biomedical and Life Sciences (SpringerNature-11642)