Systems Analytics and Integration of Big Omics Data
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 c...
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| Formatua: | Online |
| Hizkuntza: | ingelesa |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Sarrera elektronikoa: | 46008 |
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| _version_ | 1869519915597168640 |
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| author | Hardiman, Gary |
| author_browse | Hardiman, Gary |
| author_facet | Hardiman, Gary |
| author_sort | Hardiman, Gary |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-60435 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-604352024-03-30T23:22:34Z Systems Analytics and Integration of Big Omics Data Hardiman, Gary R5-920 RM1-950 precision medicine informatics n/a drug sensitivity chromatin modification cell lines biocuration neurodegeneration multivariate analysis artificial intelligence epigenetics missing data sequencing clinical data class imbalance integrative analytics algorithm development for network integration deep phenotype non-omics data feature selection Gene Ontology miRNA–gene expression networks omics data plot visualization Alzheimer’s disease tissue classification epidemiological data proteomic analysis genotype RNA expression indirect effect multi-omics dementia multiomics integration data integration phenomics network topology analysis challenges transcriptome enrichment analysis regulatory genomics scalability heterogeneous data systemic lupus erythematosus database microtubule-associated protein tau disease variants genomics joint modeling distance correlation annotation phenotype direct effect curse of dimensionality gene–environment interactions logic forest machine learning KEGG pathways multivariate causal mediation amyloid-beta bioinformatics pipelines support vector machine pharmacogenomics candidate genes tissue-specific expressed genes cognitive impairment causal inference thema EDItEUR::M Medicine and Nursing 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. 2021-02-12T05:09:24Z 2021-02-12T05:09:24Z 2020-06-09 16:38:57 2020 book 46008 9783039287444 9783039287451 https://directory.doabooks.org/handle/20.500.12854/60435 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/2183 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03928-745-1 10.3390/books978-3-03928-745-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039287444 9783039287451 202 open access |
| spellingShingle | R5-920 RM1-950 precision medicine informatics n/a drug sensitivity chromatin modification cell lines biocuration neurodegeneration multivariate analysis artificial intelligence epigenetics missing data sequencing clinical data class imbalance integrative analytics algorithm development for network integration deep phenotype non-omics data feature selection Gene Ontology miRNA–gene expression networks omics data plot visualization Alzheimer’s disease tissue classification epidemiological data proteomic analysis genotype RNA expression indirect effect multi-omics dementia multiomics integration data integration phenomics network topology analysis challenges transcriptome enrichment analysis regulatory genomics scalability heterogeneous data systemic lupus erythematosus database microtubule-associated protein tau disease variants genomics joint modeling distance correlation annotation phenotype direct effect curse of dimensionality gene–environment interactions logic forest machine learning KEGG pathways multivariate causal mediation amyloid-beta bioinformatics pipelines support vector machine pharmacogenomics candidate genes tissue-specific expressed genes cognitive impairment causal inference thema EDItEUR::M Medicine and Nursing Hardiman, Gary Systems Analytics and Integration of Big Omics Data |
| title | Systems Analytics and Integration of Big Omics Data |
| title_full | Systems Analytics and Integration of Big Omics Data |
| title_fullStr | Systems Analytics and Integration of Big Omics Data |
| title_full_unstemmed | Systems Analytics and Integration of Big Omics Data |
| title_short | Systems Analytics and Integration of Big Omics Data |
| title_sort | systems analytics and integration of big omics data |
| topic | R5-920 RM1-950 precision medicine informatics n/a drug sensitivity chromatin modification cell lines biocuration neurodegeneration multivariate analysis artificial intelligence epigenetics missing data sequencing clinical data class imbalance integrative analytics algorithm development for network integration deep phenotype non-omics data feature selection Gene Ontology miRNA–gene expression networks omics data plot visualization Alzheimer’s disease tissue classification epidemiological data proteomic analysis genotype RNA expression indirect effect multi-omics dementia multiomics integration data integration phenomics network topology analysis challenges transcriptome enrichment analysis regulatory genomics scalability heterogeneous data systemic lupus erythematosus database microtubule-associated protein tau disease variants genomics joint modeling distance correlation annotation phenotype direct effect curse of dimensionality gene–environment interactions logic forest machine learning KEGG pathways multivariate causal mediation amyloid-beta bioinformatics pipelines support vector machine pharmacogenomics candidate genes tissue-specific expressed genes cognitive impairment causal inference thema EDItEUR::M Medicine and Nursing |
| topic_facet | R5-920 RM1-950 precision medicine informatics n/a drug sensitivity chromatin modification cell lines biocuration neurodegeneration multivariate analysis artificial intelligence epigenetics missing data sequencing clinical data class imbalance integrative analytics algorithm development for network integration deep phenotype non-omics data feature selection Gene Ontology miRNA–gene expression networks omics data plot visualization Alzheimer’s disease tissue classification epidemiological data proteomic analysis genotype RNA expression indirect effect multi-omics dementia multiomics integration data integration phenomics network topology analysis challenges transcriptome enrichment analysis regulatory genomics scalability heterogeneous data systemic lupus erythematosus database microtubule-associated protein tau disease variants genomics joint modeling distance correlation annotation phenotype direct effect curse of dimensionality gene–environment interactions logic forest machine learning KEGG pathways multivariate causal mediation amyloid-beta bioinformatics pipelines support vector machine pharmacogenomics candidate genes tissue-specific expressed genes cognitive impairment causal inference thema EDItEUR::M Medicine and Nursing |
| url | 46008 |
| work_keys_str_mv | AT hardimangary systemsanalyticsandintegrationofbigomicsdata |