首页|Findings from Sorbonne University Provides New Data on Mycobacteria (Contributio n of Machine Learning for Subspecies Identification From mycobacterium Abscessus With Maldi-tof Ms In Solid and Liquid Media)
Findings from Sorbonne University Provides New Data on Mycobacteria (Contributio n of Machine Learning for Subspecies Identification From mycobacterium Abscessus With Maldi-tof Ms In Solid and Liquid Media)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Gram-Positive Bacteria - Mycobacteria. According to news reporting from Paris, F rance, by NewsRx journalists, research stated, "Mycobacterium abscessus (MABS) d isplays differential subspecies susceptibility to macrolides. Thus, identifying MABS's subspecies (M. abscessus, M. bolletii and M. massiliense) is a clinical n ecessity for guiding treatment decisions." Financial support for this research came from National Public Health Agency (San te Publique France). The news correspondents obtained a quote from the research from Sorbonne Univers ity, "We aimed to assess the potential of Machine Learning (ML)-based classifier s coupled to Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-T OF) MS to identify MABS subspecies. Two spectral databases were created by using 40 confirmed MABS strains. Spectra were obtained by using MALDITOF MS from str ains cultivated on solid (Columbia Blood Agar, CBA) or liquid (MGIT ® media for 1 to 13 days. Each database was divided into a dataset for ML-based pipeline de velopment and a dataset to assess the performance. An in-house programme was dev eloped to identify discriminant peaks specific to each subspecies. The peak-base d approach successfully distinguished M. massiliense from the other subspecies f or strains grown on CBA. The ML approach achieved 100% accuracy fo r subspecies identification on CBA, falling to 77.5% on MGIT ® Th is study validates the usefulness of ML, in particular the Random Forest algorit hm, to discriminate MABS subspecies by MALDI-TOF MS."
ParisFranceEuropeActinomycetalesCyborgsEmerging TechnologiesGram-Positive Asporogenous RodsGram-Positive B acteriaGram-Positive RodsHealth and MedicineMachine LearningMycobacteriaMycobacteriaceaeMycobacteriumSorbonne University
NETL
NSTL
