Machine Learning Methods for Small Data Challenges in Molecular Science

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable…

• NS
  National Science Foundation

  Awards: R01AI164266, 2052983, 1761320, 1900473, IIS-1900473, DMS-1761320, DMS-2052983
• NA
  National Aeronautics and Space Administration

  Award: 80NSSC21M0023
• BS
  Bristol-Myers Squibb

  Award: 65109
• P
  Pfizer
• MS
  Michigan State University Foundation
• NN
  National Natural Science Foundation of China

  Awards: IIS-1900473, 11972266, 12271416, 11971367
• NS
  Nuclear Safety and Security Commission
• NI
  National Institutes of Health

  Awards: R01GM126189, R01AI164266, IIS-1900473
• NI
  National Institute of General Medical Sciences

  Award: R01GM126189
• NI
  National Institute of Allergy and Infectious Diseases

  Award: R01AI164266
• DO
  Division of Mathematical Sciences

  Awards: DMS-1761320, DMS-2052983, DMS-1721024
• DO
  Division of Information and Intelligent Systems

  Award: IIS-1900473