[2N3-IS-2b] Machine learning (2/5)

[2N3-IS-2b-01] The identification of molecular networks on therapeutic responsiveness in AI

〇Shihori Tanabe¹,Ryuichi Ono¹, Horacio Cabral², Sabina Quader³, Ed Perkins⁴, Akihiko Hirose¹, Mitsunobu Kano⁵, Shinpei Ijichi⁶, Kohei Kessoku⁷, Hiroshi Yokozaki⁸, Hiroki Sasaki⁹ (1, Natl. Inst. of Health Sciences, 2. Univ. of Tokyo, 3. iCONM, 4. USACE ERDC, 5. Okayama Univ., 6. DataRobot Inc., 7. TECNOS Data Science Engineering Inc., 8. Kobe Univ., 9. Natl. Cancer Ctr. Res. Inst.)

Keywords:Molecular Network, AI, therapeutics

Molecular networks affect the responsiveness of diseases to therapeutics. (1) The objective of the study is to identify the molecular networks related to therapeutic responsiveness in diseases. Epithelial-mesenchymal transition (EMT) and cancer stem cells (CSCs) are involved in drug resistance in cancer, and share some molecular characteristics. To reveal the molecular networks responsible for cancer malignancy, gene expression and molecular networks in diffuse-type gastric cancer (GC), which is resistant to anti-cancer drugs, and intestinal-type GC were analyzed. Since the involvement of RNA viral network was identified in GC, the molecules and causal networks in RNA viral networks, as well as in diffuse- and intestinal-type GC were explored. CSC-related networks included glioblastoma multiforme signaling pathway. (2) Outline of the conclusions of the results: Using AI methods, we generated the candidate models including Elastic-Net Classifier (L2 / Binomial Deviance) (Cross Validation score LogLoss 0.3839, AUC 0.9037) and eXtreme Gradient Boosted Trees Classifier (Cross Validation score LogLoss 0.2647, AUC 0.9565) that can distinguish the differences between diffuse- and intestinal-type GC using molecular network data. The alteration in molecular networks may affect the therapeutic responsiveness.