biostudies-literatureUnknown14Li J<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, ChFrontiers in microbiology1157608https://www.ebi.ac.uk/biostudies/studies/S-EPMC10198438biostudies-literatureGenomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning.PMC10198438Li JTian FLiu SSFeng YLin WJiang JFJiang TTong YZhang SLi YDLei ZWei JQKang XPfalseGenomic representation predicts an asymptotic host adaptation of bat coronaviruses using deep learning.<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, Ch2023-01-01T00:00:00Z20232025-04-04T23:44:42.208Z2025-04-04T23:44:42.208ZS-EPMC101984383721351610.3389/fmicb.2023.1157608