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Despite advances in pathogen detection using deep learning, current algorithms have limitations in processing long genomic sequences. Through the deep cross-fusion of cross, residual and deep neural networks, we developed DCiPatho for accurate pathogen detection based on the integrated frequency features of 3-to-7\u00a0k-mers. Compared with the existing state-of-the-art algorithms, DCiPatho can be used to accurately identify distinct pathogenic bacteria infecting humans, animals and plants. We evaluated DCiPatho on both learned and unlearned pathogen species using both genomics and metagenomics datasets. DCiPatho is an effective tool for the genomic-scale identification of pathogens by integrating the frequency of k-mers into deep cross-fusion networks. 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Computer Science, City University of Hong Kong , Hong Kong 999077 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang-Jie","family":"Zhao","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization , Laboratory of Bio-interactions and Crop Health, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Joint International Research Laboratory of Soil Health, , Nanjing 210095, Jiangsu , China"},{"name":"Nanjing Agricultural University , Laboratory of Bio-interactions and Crop Health, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Joint International Research Laboratory of Soil Health, , Nanjing 210095, Jiangsu , 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