Molecular Complexity to Biosignatures: A Machine Learning Pipeline that Connects Mass Spectrometry to Molecular Synthesis and Reaction Networks
Molecular Complexity to Biosignatures: A Machine Learning Pipeline that Connects Mass Spectrometry to Molecular Synthesis and Reaction Networks
Jian Gong, Massachusetts Institute of Technology, Earth, Atmospheric, and Planetary Sciences, Cambridge, MA, United States, Aaron C Bell, Insight Edge Inc., 2-3-2 Otemachi, Chiyoda-ku, Tokyo-to, Japan, Timothy Gebhard, Max Planck Institute for Intelligent Systems, Empirical Inference, Tübingen, Germany; Max Planck ETH Center for Learning Systems, Max-Planck-Ring 4, Tübingen, Germany, Jaden J. A. Hastings, Weill Cornell Medicine, Dept of Biophysics and Physiology, New York, United States; Weill Cornell Medicine, The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, United States, Atılım Güneş Baydin, University of Oxford, Department of Engineering Science, Oxford, United Kingdom, Kimberly Warren-Rhodes, SETI Institute Mountain View, Mountain View, United States, Michael Phillips, Applied Physics Laboratory Johns Hopkins, Laurel, United States, Matthew Fricke, University of New Mexico, Albuquerque, United States, Nathalie A Cabrol, SETI Institute, Mountain View, CA, United States, Scott A. Sandford, NASA Ames Research Center, Moffett Field, CA, United States and Massimo Mascaro, Google, Applied AI, Mountain View, United States