Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing.

Autor: Strutt JPB; Singapore-MIT Alliance for Research and Technology , Singapore, Singapore., Natarajan M; Singapore-MIT Alliance for Research and Technology , Singapore, Singapore., Lee E; Singapore-MIT Alliance for Research and Technology , Singapore, Singapore., Teo DBL; Singapore-MIT Alliance for Research and Technology , Singapore, Singapore., Sin WX; Singapore-MIT Alliance for Research and Technology , Singapore, Singapore., Cheung KW; Singapore-MIT Alliance for Research and Technology , Singapore, Singapore., Chew M; Singapore-MIT Alliance for Research and Technology , Singapore, Singapore., Thazin K; Singapore-MIT Alliance for Research and Technology , Singapore, Singapore., Barone PW; MIT Center for Biomedical Innovation, Massachusetts Institute of Technology , Boston, USA., Wolfrum JM; MIT Center for Biomedical Innovation, Massachusetts Institute of Technology , Boston, USA., Williams RBH; Singapore-MIT Alliance for Research and Technology , Singapore, Singapore.; Singapore Centre for Environmental Life Sciences Engineering, Life Sciences Institute, National University of Singapore , Singapore, Singapore.; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University , Singapore, Singapore., Rice SA; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University , Singapore, Singapore.; CSIRO Microbiomes for One Systems Health, Agriculture and Food , Westmead, Australia., Springs SL; Singapore-MIT Alliance for Research and Technology , Singapore, Singapore.; MIT Center for Biomedical Innovation, Massachusetts Institute of Technology , Boston, USA.

Publikováno v: Microbiology spectrum [Microbiol Spectr] 2023 Aug 30, pp. e0135023. Date of Electronic Publication: 2023 Aug 30.