Amirhossein Ravari, Seyede Fatemeh Ghoreishi, Mahdi Imani
{"title":"Structure-Based Inverse Reinforcement Learning for Quantification of Biological Knowledge.","authors":"Amirhossein Ravari, Seyede Fatemeh Ghoreishi, Mahdi Imani","doi":"10.1109/cai54212.2023.00126","DOIUrl":"10.1109/cai54212.2023.00126","url":null,"abstract":"<p><p>Gene regulatory networks (GRNs) play crucial roles in various cellular processes, including stress response, DNA repair, and the mechanisms involved in complex diseases such as cancer. Biologists are involved in most biological analyses. Thus, quantifying their policies reflected in available biological data can significantly help us to better understand these complex systems. The primary challenges preventing the utilization of existing machine learning, particularly inverse reinforcement learning techniques, to quantify biologists' knowledge are the limitations and huge amount of uncertainty in biological data. This paper leverages the network-like structure of GRNs to define expert reward functions that contain exponentially fewer parameters than regular reward models. Numerical experiments using mammalian cell cycle and synthetic gene-expression data demonstrate the superior performance of the proposed method in quantifying biologists' policies.</p>","PeriodicalId":94276,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence","volume":"2023 ","pages":"282-284"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552793/pdf/nihms-1914209.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41174905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Learning to Fight Against Cell Stimuli: A Game Theoretic Perspective.","authors":"Seyed Hamid Hosseini, Mahdi Imani","doi":"10.1109/cai54212.2023.00127","DOIUrl":"https://doi.org/10.1109/cai54212.2023.00127","url":null,"abstract":"<p><p>Current genomics interventions have limitations in accounting for cell stimuli and the dynamic response to intervention. Although genomic sequencing and analysis have led to significant advances in personalized medicine, the complexity of cellular interactions and the dynamic nature of the cellular response to stimuli pose significant challenges. These limitations can lead to chronic disease recurrence and inefficient genomic interventions. Therefore, it is necessary to capture the full range of cellular responses to develop effective interventions. This paper presents a game-theoretic model of the fight between the cell and intervention, demonstrating analytically and numerically why current interventions become ineffective over time. The performance is analyzed using melanoma regulatory networks, and the role of artificial intelligence in deriving effective solutions is described.</p>","PeriodicalId":94276,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence","volume":"2023 ","pages":"285-287"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544835/pdf/nihms-1914207.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41158054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}