Reprint
Systems Analytics and Integration of Big Omics Data
Edited by
April 2020
202 pages
- ISBN978-3-03928-744-4 (Paperback)
- ISBN978-3-03928-745-1 (PDF)
This book is a reprint of the Special Issue Systems Analytics and Integration of Big Omics Data that was published in
Biology & Life Sciences
Summary
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
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
tissue-specific expressed genes; transcriptome; tissue classification; support vector machine; feature selection; bioinformatics pipelines; algorithm development for network integration; miRNA–gene expression networks; multiomics integration; network topology analysis; candidate genes; gene–environment interactions; logic forest; systemic lupus erythematosus; Gene Ontology; KEGG pathways; enrichment analysis; proteomic analysis; plot visualization; Alzheimer’s disease; dementia; cognitive impairment; neurodegeneration; Gene Ontology; annotation; biocuration; amyloid-beta; microtubule-associated protein tau; artificial intelligence; genotype; phenotype; deep phenotype; data integration; genomics; phenomics; precision medicine informatics; epigenetics; chromatin modification; sequencing; regulatory genomics; disease variants; machine learning; multi-omics; data integration; curse of dimensionality; heterogeneous data; missing data; class imbalance; scalability; genomics; pharmacogenomics; cell lines; database; drug sensitivity; data integration; omics data; genomics; RNA expression; non-omics data; clinical data; epidemiological data; challenges; integrative analytics; joint modeling; multivariate analysis; multivariate causal mediation; distance correlation; direct effect; indirect effect; causal inference; n/a