Proceedings of the Annual Conference of JSAI
Online ISSN : 2758-7347
35th (2021)
Session ID : 2N3-IS-2b-01
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The identification of molecular networks on therapeutic responsiveness in AI
*Shihori TANABERyuichi ONOHoracio CABRALSabina QUADEREd PERKINSAkihiko HIROSEMitsunobu KANOShinpei IJICHIKohei KESSOKUHiroshi YOKOZAKIHiroki SASAKI
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Abstract

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.

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© 2021 The Japanese Society for Artificial Intelligence
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