01094nam--2200349---450-9900005138802033160-471-25547-50051388USA010051388(ALEPH)000051388USA01005138820010618d1998----km-y0itay0103----baengUS||||||||001yy<<The>> data warehouse lifecycle toolkitexpert methods for designing, developing and deploying data warehousesRalph Kimball... et al.New YorkJ. Wiley & sonsc1998XVIII, 771 p.24 cm1 CD-ROM2001AziendeServizi di informazioneAutomazione658.40380285574KIMBALL,RalphITsalbcISBD990000513880203316658.403 8 DAT10684 ING658.403 8BKTECPATTY9020010618USA01120620020403USA011700PATRY9020040406USA011636Data warehouse lifecycle toolkit886860UNISA04530nam 2201153z- 450 9910404089603321202102123-03928-745-1(CKB)4100000011302242(oapen)https://directory.doabooks.org/handle/20.500.12854/60435(oapen)doab60435(oapen)60435(EXLCZ)99410000001130224220202102d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierSystems Analytics and Integration of Big Omics DataMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (202 p.)3-03928-744-3 A "genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This "Big Data" is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene-environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.MedicinebicsscMedicine and Nursingbicsscalgorithm development for network integrationAlzheimer's diseaseamyloid-betaannotationartificial intelligencebiocurationbioinformatics pipelinescandidate genescausal inferencecell lineschallengeschromatin modificationclass imbalanceclinical datacognitive impairmentcurse of dimensionalitydata integrationdatabasedeep phenotypedementiadirect effectdisease variantsdistance correlationdrug sensitivityenrichment analysisepidemiological dataepigeneticsfeature selectionGene Ontologygene-environment interactionsgenomicsgenotypeheterogeneous dataindirect effectintegrative analyticsjoint modelingKEGG pathwayslogic forestmachine learningmicrotubule-associated protein taumiRNA-gene expression networksmissing datamulti-omicsmultiomics integrationmultivariate analysismultivariate causal mediationn/anetwork topology analysisneurodegenerationnon-omics dataomics datapharmacogenomicsphenomicsphenotypeplot visualizationprecision medicine informaticsproteomic analysisregulatory genomicsRNA expressionscalabilitysequencingsupport vector machinesystemic lupus erythematosustissue classificationtissue-specific expressed genestranscriptomeMedicineMedicine and NursingHardiman Garyauth1314898BOOK9910404089603321Systems Analytics and Integration of Big Omics Data3032113UNINA