[2S4-GS-2] Machine learning:

[2S4-GS-2-02] Artificial Intelligence-based modeling of coronavirus pathway activation

〇Shihori Tanabe¹, Sabina Quader², Ryuichi Ono¹, Horacio Cabral³, Edward J Perkins⁴ (1. National Institute of Health Sciences, 2. iCONM, 3. The Univ. of Tokyo, 4. US Army Engineer Research and Development Center)

[[Online]]

Keywords:machine learning, AI prediction, coronavirus, molecular pathway, pathway analysis

Coronavirus molecular pathways are activated upon coronaviral infection. An artificial intelligence (AI) approach based on machine learning was utilized to develop models with images of the coronavirus pathogenesis pathway to predict the activation states of the coronavirus molecular pathways. Among more than 100,000 analyses and datasets in the Ingenuity Pathway Analysis (IPA) database, 106 analyses and 106 datasets were involved in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as of 2021. A total of 22 analyses in SARS-CoV-2 infected lung adenocarcinoma (LUAD) were identified to be related to the terms “human” and “SARS coronavirus 2” in the database. The coronavirus pathogenesis pathway was activated in SARS-CoV-2-infected LUAD cells. The prediction model was developed in Python using images of coronavirus pathogenesis pathways in different conditions. The prediction model of activation states of coronavirus molecular pathways may be useful for treatment identification.