TY - BOOK ID - 64931113 TI - Systems Analytics and Integration of Big Omics Data PY - 2020 SN - 3039287451 3039287443 PB - MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - precision medicine informatics KW - n/a KW - drug sensitivity KW - chromatin modification KW - cell lines KW - biocuration KW - neurodegeneration KW - multivariate analysis KW - artificial intelligence KW - epigenetics KW - missing data KW - sequencing KW - clinical data KW - class imbalance KW - integrative analytics KW - algorithm development for network integration KW - deep phenotype KW - non-omics data KW - feature selection KW - Gene Ontology KW - miRNA–gene expression networks KW - omics data KW - plot visualization KW - Alzheimer’s disease KW - tissue classification KW - epidemiological data KW - proteomic analysis KW - genotype KW - RNA expression KW - indirect effect KW - multi-omics KW - dementia KW - multiomics integration KW - data integration KW - phenomics KW - network topology analysis KW - challenges KW - transcriptome KW - enrichment analysis KW - regulatory genomics KW - scalability KW - heterogeneous data KW - systemic lupus erythematosus KW - database KW - microtubule-associated protein tau KW - disease variants KW - genomics KW - joint modeling KW - distance correlation KW - annotation KW - phenotype KW - direct effect KW - curse of dimensionality KW - gene–environment interactions KW - logic forest KW - machine learning KW - KEGG pathways KW - multivariate causal mediation KW - amyloid-beta KW - bioinformatics pipelines KW - support vector machine KW - pharmacogenomics KW - candidate genes KW - tissue-specific expressed genes KW - cognitive impairment KW - causal inference KW - miRNA-gene expression networks KW - Alzheimer's disease KW - gene-environment interactions UR - https://www.unicat.be/uniCat?func=search&query=sysid:64931113 AB - 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. ER -