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UID:8060@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20150529T140000
DTEND;TZID=Europe/Paris:20150529T150000
DTSTAMP:20241120T210011Z
URL:https://www.i2m.univ-amu.fr/evenements/pierre-mahe-biomerieux-machine-
 learning-and-clinical-microbiology-some-applications-for-in-vitro-diagnost
 ics/
SUMMARY: (...): Pierre Mahé (bioMérieux): Machine learning and clinical m
 icrobiology : some applications for in vitro diagnostics
DESCRIPTION:: Title: Machine learning and clinical microbiology : some appl
 ications for in vitro diagnostics\n\nAbstract: High-throughput technologie
 s like mass-spectrometry and next-generation sequencing offer powerful mea
 ns to characterize microorganisms\\\, and are receiving an increasing atte
 ntion in the field of clinical microbiology. They are in particular increa
 singly accepted in the context in vitro diagnostics\\\, where machine lear
 ning techniques become key to cast the large and/or complex volumes of dat
 a produced into a high-level result\\\, ultimately actionable by a clinici
 an to prescribe a therapy.\n \nIn this talk I will present two contributio
 ns recently made by bioMérieux in this area. The first topic I will discu
 ss is related to the task of detecting and identifying polymicrobial sampl
 es from a MALDI-TOF mass spectrum\\\, for the purpose of which we rely on 
 a penalized non-negative linear regression framework. I will then discuss 
 how large-scale machine learning methods can be useful to estimate the con
 stitution of the bacterial flora present in a sample from next-generation 
 sequencing reads\\\, using techniques inherited from the text processing l
 iterature.
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DTSTART:20150329T030000
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