Regulation of Epithelial–Mesenchymal Transition Pathway and Artificial Intelligence-Based Modeling for Pathway Activity Prediction
Because activity of the epithelial–mesenchymal transition (EMT) is involved in anti-cancer drug resistance, cancer malignancy, and shares some characteristics with cancer stem cells (CSCs), we used artificial intelligence (AI) modeling to identify the cancer-related activity of the EMT-related pathw...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Online Access: | https://www.mdpi.com/2673-7523/3/1/2 |
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| author | Shihori Tanabe Sabina Quader Ryuichi Ono Horacio Cabral Kazuhiko Aoyagi Akihiko Hirose Edward J. Perkins Hiroshi Yokozaki Hiroki Sasaki |
| author_facet | Shihori Tanabe Sabina Quader Ryuichi Ono Horacio Cabral Kazuhiko Aoyagi Akihiko Hirose Edward J. Perkins Hiroshi Yokozaki Hiroki Sasaki |
| author_sort | Shihori Tanabe |
| collection | DOAJ |
| description | Because activity of the epithelial–mesenchymal transition (EMT) is involved in anti-cancer drug resistance, cancer malignancy, and shares some characteristics with cancer stem cells (CSCs), we used artificial intelligence (AI) modeling to identify the cancer-related activity of the EMT-related pathway in datasets of gene expression. We generated images of gene expression overlayed onto molecular pathways with Ingenuity Pathway Analysis (IPA). A dataset of 50 activated and 50 inactivated pathway images of EMT regulation in the development pathway was then modeled by the DataRobot Automated Machine Learning platform. The most accurate models were based on the Elastic-Net Classifier algorithm. The model was validated with 10 additional activated and 10 additional inactivated pathway images. The generated models had false-positive and false-negative results. These images had significant features of opposite labels, and the original data were related to Parkinson’s disease. This approach reliably identified cancer phenotypes and treatments where EMT regulation in the development pathway was activated or inactivated thereby identifying conditions where therapeutics might be applied or developed. As there are a wide variety of cancer phenotypes and CSC targets that provide novel insights into the mechanism of CSCs’ drug resistance and cancer metastasis, our approach holds promise for modeling and simulating cellular phenotype transition, as well as predicting molecular-induced responses. |
| first_indexed | 2024-03-11T06:04:35Z |
| format | Article |
| id | doaj.art-cb6784a71e6743489888765207563c89 |
| institution | Directory Open Access Journal |
| issn | 2673-7523 |
| language | English |
| last_indexed | 2024-03-11T06:04:35Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Onco |
| spelling | doaj.art-cb6784a71e6743489888765207563c892023-11-17T13:07:33ZengMDPI AGOnco2673-75232023-01-0131132510.3390/onco3010002Regulation of Epithelial–Mesenchymal Transition Pathway and Artificial Intelligence-Based Modeling for Pathway Activity PredictionShihori Tanabe0Sabina Quader1Ryuichi Ono2Horacio Cabral3Kazuhiko Aoyagi4Akihiko Hirose5Edward J. Perkins6Hiroshi Yokozaki7Hiroki Sasaki8Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, JapanInnovation Center of NanoMedicine (iCONM), Kawasaki Institute of Industrial Promotion, Kawasaki 210-0821, JapanDivision of Cellular and Molecular Toxicology, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, JapanDepartment of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-0033, JapanDepartment of Clinical Genomics, National Cancer Center Research Institute, Tokyo 104-0045, JapanDivision of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, JapanEnvironmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 39180, USADepartment of Pathology, Kobe University of Graduate School of Medicine, Kobe 650-0017, JapanDepartment of Translational Oncology, National Cancer Center Research Institute, Tokyo 104-0045, JapanBecause activity of the epithelial–mesenchymal transition (EMT) is involved in anti-cancer drug resistance, cancer malignancy, and shares some characteristics with cancer stem cells (CSCs), we used artificial intelligence (AI) modeling to identify the cancer-related activity of the EMT-related pathway in datasets of gene expression. We generated images of gene expression overlayed onto molecular pathways with Ingenuity Pathway Analysis (IPA). A dataset of 50 activated and 50 inactivated pathway images of EMT regulation in the development pathway was then modeled by the DataRobot Automated Machine Learning platform. The most accurate models were based on the Elastic-Net Classifier algorithm. The model was validated with 10 additional activated and 10 additional inactivated pathway images. The generated models had false-positive and false-negative results. These images had significant features of opposite labels, and the original data were related to Parkinson’s disease. This approach reliably identified cancer phenotypes and treatments where EMT regulation in the development pathway was activated or inactivated thereby identifying conditions where therapeutics might be applied or developed. As there are a wide variety of cancer phenotypes and CSC targets that provide novel insights into the mechanism of CSCs’ drug resistance and cancer metastasis, our approach holds promise for modeling and simulating cellular phenotype transition, as well as predicting molecular-induced responses.https://www.mdpi.com/2673-7523/3/1/2artificial intelligenceepithelial–mesenchymal transitionIngenuity Pathway Analysismachine learningmolecular pathway network |
| spellingShingle | Shihori Tanabe Sabina Quader Ryuichi Ono Horacio Cabral Kazuhiko Aoyagi Akihiko Hirose Edward J. Perkins Hiroshi Yokozaki Hiroki Sasaki Regulation of Epithelial–Mesenchymal Transition Pathway and Artificial Intelligence-Based Modeling for Pathway Activity Prediction Onco artificial intelligence epithelial–mesenchymal transition Ingenuity Pathway Analysis machine learning molecular pathway network |
| title | Regulation of Epithelial–Mesenchymal Transition Pathway and Artificial Intelligence-Based Modeling for Pathway Activity Prediction |
| title_full | Regulation of Epithelial–Mesenchymal Transition Pathway and Artificial Intelligence-Based Modeling for Pathway Activity Prediction |
| title_fullStr | Regulation of Epithelial–Mesenchymal Transition Pathway and Artificial Intelligence-Based Modeling for Pathway Activity Prediction |
| title_full_unstemmed | Regulation of Epithelial–Mesenchymal Transition Pathway and Artificial Intelligence-Based Modeling for Pathway Activity Prediction |
| title_short | Regulation of Epithelial–Mesenchymal Transition Pathway and Artificial Intelligence-Based Modeling for Pathway Activity Prediction |
| title_sort | regulation of epithelial mesenchymal transition pathway and artificial intelligence based modeling for pathway activity prediction |
| topic | artificial intelligence epithelial–mesenchymal transition Ingenuity Pathway Analysis machine learning molecular pathway network |
| url | https://www.mdpi.com/2673-7523/3/1/2 |
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