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003			ES-MaUEC
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007			cr nn 008mamaa
008			230115s2023 si | o |||| 0|eng d
020			_a9789811982101
024	7		_a10.1007/978-981-19-8210-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D338 _b2023 EB
245	0	0	_aMethodologies of Multi-Omics Data Integration and Data Mining : _bTechniques and Applications _cedited by Kang Ning
250			_a1st ed. 2023
264		1	_aSingapore _bSpringer Nature Singapore _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aTranslational Bioinformatics _x2213-2783 _v19
505	0		_aChapter 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			_aThis 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.
988			_aSpringer_BiomedLife_2023
650		7	_2embne _9495511 _aDatos masivos
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-8210-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2024 _dz _eIG _zSI