01317nam0 22003131i 450 UON0007226620231205102355.49935-15-04205-920020107d1986 |0itac50 baengDE|||| |||||ˆA ‰Yemenite Embassy to Ethiopia1647-1649Al-Haymi's Sirat Al-Habasha newly introduced, translated and annotatedEmeri Johannes Van DonzelStuttgartFranz Steiner1986252 p.24 cm001UON000656852001 Aethiopistische ForschungenHerausgegeben von Ernst Hammerschmidt21UON00359181Sirat Al-HabashaISLAMYemenUONC019432FIDEWiesbadenUONL003153297.1975RELAZIONI DELL'ISLAM CON LE DISCIPLINE SECOLARI21ˆal-‰HAYMIUONV047373658729DONZELEmeri Johannes : vanUONV046544SteinerUONV245907650ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00072266SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI ET VI F a 008 SI AA 8355 7 008 Yemenite Embassy to Ethiopia1159368UNIOR03488nam 2200781z- 450 991055754580332120210501(CKB)5400000000044149(oapen)https://directory.doabooks.org/handle/20.500.12854/68899(oapen)doab68899(EXLCZ)99540000000004414920202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierStatistical Methods for the Analysis of Genomic DataBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (136 p.)3-03936-140-6 3-03936-141-4 In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.Mathematics and SciencebicsscResearch and information: generalbicsscBayes factorBayesian mixed-effect modelboostingclassificationclassification boundaryclustering analysisconvolutional neural networksCpG sitesdeep learningDNA methylationexpectation-maximization algorithmfalse discovery rate controlfeed-forward neural networksgaussian finite mixture modelGEEgene expressiongene regulatory networkgene set enrichment analysisintegrative analysiskernel methodlipid-environment interactionlongitudinal lipidomics studymachine learningmultiple cancer typesn/anetwork substructurenonparanormal graphical modelomics dataOrdinal responsespenalized variable selectionprognosis modelingRNA-sequncertaintyMathematics and ScienceResearch and information: generalJiang Huiedt1312123He ZhiedtJiang HuiothHe ZhiothBOOK9910557545803321Statistical Methods for the Analysis of Genomic Data3030716UNINA