00874nam0 22002411i 450 UON0011188720231205102640.29320020107d1985 |0itac50 baengIN|||| 1||||Indo-Aryan linguisticscollected papers 1912-1973R.L. TurnerDelhiDisha Publications1985XI, 435 p.INNew DelhiUONL000110TURNERR. L.UONV070746666313Disha PublicationsUONV261704650ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00111887SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI GLOTT C 1 II 008 SI MC 12065 5 008 Indo-Aryan linguistics1308138UNIOR04419nam 2201117z- 450 9910404089603321202102123-03928-745-1(CKB)4100000011302242(oapen)https://directory.doabooks.org/handle/20.500.12854/60435(oapen)doab60435(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.Medicinebicsscalgorithm 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 genestranscriptomeMedicineHardiman Garyauth1314898BOOK9910404089603321Systems Analytics and Integration of Big Omics Data3032113UNINA