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DTSTAMP:20250514T184749Z
DESCRIPTION:Platforms to improve computational reproducibility in biomedica
 l research\n\nBenjamin Haibe-Kains\, University of Toronto\n	Tuesday Februa
 ry 2\, 12-1pm\n	Zoom Link: https:/mcgill.zoom.us/j/91589192037\n\nAbstract:
  As machine learning becomes a method of choice to analyze biomedical data
 \, the field is facing multiple challenges around research reproducibility
  and transparency. Given the proliferation of studies investigating the ap
 plications of machine learning in biomedical research studies\, it is esse
 ntial for independent researchers to be able to scrutinize and reproduce t
 he results of a study using its materials\, and build upon them in future 
 studies. Computational reproducibility is achievable when the data can eas
 ily be shared and the required computational resources are relatively comm
 on. However\, the complexity of AI algorithms and their implementation\, t
 he need for specific computer hardware and the use of sensitive biomedical
  data represent major obstacles in healthy-related research. In this talk\
 , I will describe the various aspects of a biomedical study using machine 
 learning that are necessary for reproducibility and the platforms that exi
 st for sharing these materials with the scientific community.\n
DTSTART:20210202T170000Z
DTEND:20210202T180000Z
LOCATION:CA\, QC
SUMMARY:QLS Seminar Series - Benjamin Haibe-Kains
URL:/qls/channels/event/qls-seminar-series-benjamin-ha
 ibe-kains-327824
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