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Detection of Mixed Infection from Bacterial Whole Genome Sequence Data Allows Assessment of Its Role in Clostridium difficile Transmission., , , , , , and . PLoS Comput. Biol., (2013)Characterisation of Clostridium difficile Hospital Ward–Based Transmission Using Extensive Epidemiological Data and Molecular Typing, , , , , , , , , and 2 other author(s). PLoS Med, 9 (2): e1001172+ (Feb 7, 2012)DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis., , , , , , , , , and . Bioinform., 35 (18): 3240-3249 (2019)Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data., , , , , , , , , and 1 other author(s). Bioinform., 34 (10): 1666-1671 (2018)An efficient record linkage scheme using graphical analysis for identifier error detection., , , and . BMC Medical Informatics Decis. Mak., (2011)Measuring distance through dense weighted networks: The case of hospital-associated pathogens., , , , , and . PLoS Comput. Biol., (2017)Viral load in community SARS-CoV-2 cases varies widely and temporally, , , , , , , , , and 8 other author(s). (October 2020)Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission., , , , and . PLoS Comput. Biol., (2021)Application of machine learning techniques to tuberculosis drug resistance analysis., , , , , , , , and . Bioinform., 35 (13): 2276-2282 (2019)