DCiPatho: deep cross-fusion networks for genome scale identification of pathogens
Gaofei Jiang, Jiaxuan Zhang, Yaozhong Zhang, Xinrun Yang, Tingting Li, Ningqi Wang, Xingjian Chen, Fang‐Jie Zhao, Zhong Wei, Yangchun Xu, Qirong Shen, Wei Xue
Abstract
Pathogen detection from biological and environmental samples is important for global disease control. 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 k-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. The source code is publicly available at https://github.com/LorMeBioAI/DCiPatho.