Reprint
Computational Methods for the Analysis of Genomic Data and Biological Processes
Edited by
February 2021
222 pages
- ISBN978-3-03943-771-9 (Hardback)
- ISBN978-3-03943-772-6 (PDF)
This book is a reprint of the Special Issue Computational Methods for the Analysis of Genomic Data and Biological Processes that was published in
Biology & Life Sciences
Summary
In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
HIGD2A; cancer; DNA methylation; mRNA expression; miRNA; quercetin; hypoxia; eQTL; CRISPR-Cas9; single-cell clone; fine-mapping; power; RNA N6-methyladenosine site; yeast genome; methylation; computational biology; deep learning; bioinformatics; hepatocellular carcinoma; transcriptomics; proteomics; bioinformatics analysis; differentiation; Gene Ontology; Reactome Pathways; gene-set enrichment; meta-analysis; transcription factor; binding sites; genomics; transcriptomics; chilling stress; CBF; DREB; CAMTA1; pathway; meta-analysis; text mining; infiltration tactics optimization algorithm; classification; clustering; cancer; microarray; ensembles; machine learning; infiltration; computational intelligence; gene co-expression network; murine coronavirus; viral infection; immune response; data mining; systems biology; obesity; differential genes expression; exercise; high-fat diet; pathways; potential therapeutic targets; DNA N6-methyladenine; Chou’s 5-steps rule; Convolution Neural Network (CNN); Long Short-Term Memory (LSTM); computational biology; machine-learning; chromatin interactions; prediction; genomics; genome architecture; n/a