LEADER 04396nam 2201189z- 450 001 9910557129603321 005 20210501 035 $a(CKB)5400000000040758 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68364 035 $a(oapen)doab68364 035 $a(EXLCZ)995400000000040758 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aComputational Methods for the Analysis of Genomic Data and Biological Processes 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (222 p.) 311 08$a3-03943-771-2 311 08$a3-03943-772-0 330 $aIn 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. 606 $aBiology, life sciences$2bicssc 606 $aResearch & information: general$2bicssc 610 $abinding sites 610 $abioinformatics 610 $abioinformatics analysis 610 $aCAMTA1 610 $acancer 610 $aCBF 610 $achilling stress 610 $aChou's 5-steps rule 610 $achromatin interactions 610 $aclassification 610 $aclustering 610 $acomputational biology 610 $acomputational intelligence 610 $aConvolution Neural Network (CNN) 610 $aCRISPR-Cas9 610 $adata mining 610 $adeep learning 610 $adifferential genes expression 610 $adifferentiation 610 $aDNA methylation 610 $aDNA N6-methyladenine 610 $aDREB 610 $aensembles 610 $aeQTL 610 $aexercise 610 $afine-mapping 610 $agene co-expression network 610 $aGene Ontology 610 $agene-set enrichment 610 $agenome architecture 610 $agenomics 610 $ahepatocellular carcinoma 610 $aHIGD2A 610 $ahigh-fat diet 610 $ahypoxia 610 $aimmune response 610 $ainfiltration 610 $ainfiltration tactics optimization algorithm 610 $aLong Short-Term Memory (LSTM) 610 $amachine learning 610 $amachine-learning 610 $ameta-analysis 610 $amethylation 610 $amicroarray 610 $amiRNA 610 $amRNA expression 610 $amurine coronavirus 610 $an/a 610 $aobesity 610 $apathway 610 $apathways 610 $apotential therapeutic targets 610 $apower 610 $aprediction 610 $aproteomics 610 $aquercetin 610 $aReactome Pathways 610 $aRNA N6-methyladenosine site 610 $asingle-cell clone 610 $asystems biology 610 $atext mining 610 $atranscription factor 610 $atranscriptomics 610 $aviral infection 610 $ayeast genome 615 7$aBiology, life sciences 615 7$aResearch & information: general 700 $aGo?mez Vela$b Francisco A$4edt$00 702 $aDivina$b Federico$4edt 702 $aGarci?a-Torres$b Miguel$4edt 702 $aGo?mez Vela$b Francisco A$4oth 702 $aDivina$b Federico$4oth 702 $aGarci?a-Torres$b Miguel$4oth 906 $aBOOK 912 $a9910557129603321 996 $aComputational Methods for the Analysis of Genomic Data and Biological Processes$93034921 997 $aUNINA