[2U1-IS-1b] Knowledge engineering

[2U1-IS-1b-04] Artificial Intelligence-Based Models to Predict the Activation State of Molecular Pathway in Diseases

〇Shihori Tanabe¹, Sabina Quader², Ryuichi Ono¹, Horacio Cabral³, Kazuhiko Aoyagi⁴, Akihiko Hirose¹, Edward J Perkins⁵, Hiroshi Yokozaki⁶, Hiroki Sasaki⁴ (1. National Institute of Health Sciences, 2. Innovation Center of NanoMedicine (iCONM), 3. The Univ. of Tokyo, 4. National Cancer Center Research Institute, 5. US Army Engineer Research and Development Center, 6. Kobe Univ.)

[[Online, Regular]]

Keywords:Molecular Pathway, Activity Prediction, Modeling

(1) The objective of this study is to generate artificial intelligence (AI)-based models to predict the activation state of the molecular pathway networks. (2) Since the activity of the epithelial-mesenchymal transition (EMT) is involved in anti-cancer drug resistance and cancer stem cells, we used AI modeling to identify the cancer-related activity of the EMT-related pathway in datasets of gene expression. Molecular network pathway analyses were performed on the gene expression data of diffuse- and intestinal-type gastric cancer. A dataset of 50 activated and 50 inactivated pathway images of EMT regulation by growth factors pathway was modeled by the DataRobot Automated Machine Learning platform. The AI application created a Light Gradient Boosted Trees Regressor model to predict the activation state of the EMT pathway. The model was validated with 10 additional activated and 10 additional inactivated pathway images. Our approach holds promise for modeling and simulating cellular phenotype transition.