ML Tea: Bridging machine learning and optimization with computational metabolomics

Speaker: Runzhong Wang

Title: Bridging machine learning and optimization with computational metabolomics

Abstract:
(This is a practice job talk)
Solving hard optimization problems has been a longstanding challenge in computer science and beyond. Machine learning-based solvers turned out to be a promising direction, where problem patterns with certain distributions are captured to facilitate faster and more accurate problem-solving. We studied the theory and methodology of machine learning solvers for permutation-based combinatorial optimization and demonstrated the superiority of machine learning over existing methods. Going beyond, we transferred the insights to a long-standing problem in science with combinatorial nature—inferring molecular structures from liquid chromatography tandem mass spectrometry, a current bottleneck in computational metabolomics. We developed neural networks for in silico fragmentation, surpassing existing approaches by a significant margin, achieving 40% accuracy for annotating the exact structure as the top prediction and 92% accuracy in top 10. We demonstrated the utility of our approach in life science, environmental science, chemistry, and biology by real-world case studies. We expect the continuation of the research will not only enable new capabilities in science but also establish new insights in machine learning research.