{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14"],"submitter":["Li J"],"pubmed_abstract":["<h4>Introduction</h4>Coronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals.<h4>Methods</h4>The CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, <i>ORF1ab</i> and <i>Spike</i>. DCR features were first analyzed for their distribution among adaptive hosts and then trained with a DL classifier of convolutional neural networks (CNN) to predict the adaptation of bat CoVs.<h4>Results and discussion</h4>The results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Ch"],"journal":["Frontiers in microbiology"],"pagination":["1157608"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10198438"],"repository":["biostudies-literature"],"pubmed_title":["Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning."],"pmcid":["PMC10198438"],"pubmed_authors":["Li J","Tian F","Liu SS","Feng Y","Lin W","Jiang JF","Jiang T","Tong Y","Zhang S","Li YD","Lei Z","Wei JQ","Kang XP"],"additional_accession":[]},"is_claimable":false,"name":"Genomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning.","description":"<h4>Introduction</h4>Coronaviruses (CoVs) are naturally found in bats and can occasionally cause infection and transmission in humans and other mammals. Our study aimed to build a deep learning (DL) method to predict the adaptation of bat CoVs to other mammals.<h4>Methods</h4>The CoV genome was represented with a method of dinucleotide composition representation (DCR) for the two main viral genes, <i>ORF1ab</i> and <i>Spike</i>. DCR features were first analyzed for their distribution among adaptive hosts and then trained with a DL classifier of convolutional neural networks (CNN) to predict the adaptation of bat CoVs.<h4>Results and discussion</h4>The results demonstrated inter-host separation and intra-host clustering of DCR-represented CoVs for six host types: Artiodactyla, Carnivora, Ch","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2025-04-04T23:44:42.208Z","creation":"2025-04-04T23:44:42.208Z"},"accession":"S-EPMC10198438","cross_references":{"pubmed":["37213516"],"doi":["10.3389/fmicb.2023.1157608"]}}