02062oam 2200589M 450 991071647680332120200213070935.3(CKB)5470000002521375(OCoLC)1065616087(OCoLC)995470000002521375(EXLCZ)99547000000252137520071213d1927 ua 0engurcn|||||||||txtrdacontentcrdamediacrrdacarrierReconnaissance work in the Rio Grande Valley. January 18, 1927. -- Committed to the Committee of the Whole House on the State of the Union and ordered to be printed[Washington, D.C.] :[U.S. Government Printing Office],1927.1 online resource (5 pages)House report / 69th Congress, 2nd session. House ;no. 1794[United States congressional serial set] ;[serial no. 8688]Batch processed record: Metadata reviewed, not verified. Some fields updated by batch processes.FDLP item number not assigned.DrainageIndians of North AmericaLand tenureIrrigationReclamation of landSurveyingWater districtsIndiansPaymentLegislative materials.lcgftDrainage.Indians of North AmericaLand tenure.Irrigation.Reclamation of land.Surveying.Water districts.Indians.Payment.Morrow John1865-1935Democrat (NM)1389110WYUWYUOCLCOOCLCQOCLCOOCLCQBOOK9910716476803321Reconnaissance work in the Rio Grande Valley. January 18, 1927. -- Committed to the Committee of the Whole House on the State of the Union and ordered to be printed3444474UNINA03488nam 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