4 Topic Commentaries

Machine Learning Alone Won’t Take Vibrational Spectroscopy Into the Clinic

Machine Learning Alone Won’t Take Vibrational Spectroscopy Into the Clinic

• 

  Koseki Kobayashi-Kirschvink, PhD

  Biophotonics and computational biology

  •

  Massachusetts Institute of Technology and Broad Institute of MIT and Harvard

  “RNA sequencing gives you extremely detailed information, but it’s destructive. Raman is noninvasive, but it doesn’t tell you anything about RNA. So, the idea of this project was to use machine learning to combine the strength of both modalities, thereby allowing you to understand the dynamics of gene expression profiles at the single cell level over time,”

  Source
• 

  Jeon Woong Kang, PhD

  Biomedical optics and Raman spectroscopy

  •

  Massachusetts Institute of Technology, Laser Biomedical Research Center

  “One of the biggest advantages of Raman is that it’s a label-free method. It’s a long way off, but there is potential for the human translation, which could not be done using the existing invasive techniques for measuring genomic profiles,”

  Source
• 

  Ishan Barman, PhD, SM

  Biomedical imaging and spectroscopy

  •

  Johns Hopkins University

  “You need to prove beyond a doubt that the differences that you’re seeing are immune checkpoint inhibitor-induced as opposed to just differences between two individuals,”

  Source
• 

  James W. Tunnell, PhD

  Biomedical optical spectroscopy and imaging

  •

  The University of Texas at Austin

  “This probe, which is able to combine all three spectral modalities, is the next critical step to translating spectroscopic technology to the clinic,”

  Source