[4Q3-IS-2d] Machine learning

[4Q3-IS-2d-03] Artificial Intelligence-Based Models to Predict the Activation State of Coronavirus Molecular Pathway Networks

〇Shihori Tanabe¹, Sabina Quader², Ryuichi Ono¹, Horacio Cabral³, Kazuhiko Aoyagi⁴, Edward J Perkins⁵, Hiroshi Yokozaki⁶, Hiroki Sasaki⁴ (1. National Institute of Health Sciences, 2. Innovation Center of NanoMedicine, 3. The Univ. of Tokyo, 4. National Cancer Center Research Institute, 5. US Army Engineer Research and Development Center, 6. Kobe Univ.)

Keywords:Molecular Network, Coronavirus, Machine Learning

(1) The aim of this study is to develop artificial intelligence (AI)-based models for predicting the activation state of coronavirus molecular pathway networks. (2) Previously, we developed AI-based models to predict the activation state of epithelial-mesenchymal transition (EMT) in cancer. In this current study, a dataset comprising 50 activated and 50 inactivated pathway images for the coronavirus pathogenesis pathway, along with 50 activated and 50 inactivated pathway images for the coronavirus replication pathway, were utilized to train models using the DataRobot Automated Machine Learning platform. The AI application produced an Elastic-Net Classifier (L2 / Binomial Deviance) for the coronavirus pathogenesis pathway and an Elastic-Net Classifier (mixing alpha=0.5 / Binomial Deviance) for the coronavirus replication pathway. To validate the models, 10 additional activated and 10 additional inactivated pathway images were used. The models successfully predicted the activation state of the molecular pathway networks.