Computational Methods for the Analysis of Genomic Data and Biological Processes
Gómez Vela, Francisco A.
Computational Methods for the Analysis of Genomic Data and Biological Processes - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (222 p.)
Open Access
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.
Creative Commons
English
books978-3-03943-772-6 9783039437719 9783039437726
10.3390/books978-3-03943-772-6 doi
Research & information: general
Biology, life sciences
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 chilling stress CBF DREB CAMTA1 pathway text mining infiltration tactics optimization algorithm classification clustering 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) machine-learning chromatin interactions prediction genome architecture n/a
Computational Methods for the Analysis of Genomic Data and Biological Processes - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (222 p.)
Open Access
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.
Creative Commons
English
books978-3-03943-772-6 9783039437719 9783039437726
10.3390/books978-3-03943-772-6 doi
Research & information: general
Biology, life sciences
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 chilling stress CBF DREB CAMTA1 pathway text mining infiltration tactics optimization algorithm classification clustering 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) machine-learning chromatin interactions prediction genome architecture n/a