LEADER 03594nam 2200493 450 001 9910644258303321 005 20230517145626.0 010 $a9789811982101$b(electronic bk.) 010 $z9789811982095 024 7 $a10.1007/978-981-19-8210-1 035 $a(MiAaPQ)EBC7178704 035 $a(Au-PeEL)EBL7178704 035 $a(CKB)26019232200041 035 $a(DE-He213)978-981-19-8210-1 035 $a(PPN)267808879 035 $a(EXLCZ)9926019232200041 100 $a20230517d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMethodologies of multi-omics data integration and data mining $etechniques and applications /$fKang Ning 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer,$d[2023] 210 4$dİ2023 215 $a1 online resource (173 pages) 225 1 $aTranslational Bioinformatics,$x2213-2783 ;$v19 311 08$aPrint version: Ning, Kang Methodologies of Multi-Omics Data Integration and Data Mining Singapore : Springer,c2023 9789811982095 327 $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. 330 $aThis book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the ?What?, ?Why? and ?How? of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches. 410 0$aTranslational Bioinformatics,$x2213-2783 ;$v19 606 $aData integration (Computer science) 606 $aData mining$xMethodology 615 0$aData integration (Computer science) 615 0$aData mining$xMethodology. 676 $a005.73 700 $aNing$b Kang$01354601 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910644258303321 996 $aMethodologies of Multi-Omics Data Integration and Data Mining$93344814 997 $aUNINA