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UNINA9910644258303321 |
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Autore |
Ning Kang |
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Titolo |
Methodologies of multi-omics data integration and data mining : techniques and applications / / Kang Ning |
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Pubbl/distr/stampa |
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Singapore : , : Springer, , [2023] |
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©2023 |
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ISBN |
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9789811982101 |
9789811982095 |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (173 pages) |
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Collana |
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Translational Bioinformatics, , 2213-2783 ; ; 19 |
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Disciplina |
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Soggetti |
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Data integration (Computer science) |
Data mining - Methodology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. |
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