Stem cell differentiation platform utilizing Bayesian machine learning
| Project/Area Number |
20KK0160
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| Research Category |
Fund for the Promotion of Joint International Research (Fostering Joint International Research (B))
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 44:Biology at cellular to organismal levels, and related fields
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
安井 孝介 京都大学, 高等研究院, 特定助教 (10877640)
ABDALKADER Rodi 立命館大学, 立命館グローバル・イノベーション研究機構, 准教授 (20839964)
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| Project Period (FY) |
2020-10-27 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥18,850,000 (Direct Cost: ¥14,500,000、Indirect Cost: ¥4,350,000)
Fiscal Year 2023: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2022: ¥7,280,000 (Direct Cost: ¥5,600,000、Indirect Cost: ¥1,680,000)
Fiscal Year 2021: ¥7,280,000 (Direct Cost: ¥5,600,000、Indirect Cost: ¥1,680,000)
Fiscal Year 2020: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
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| Keywords | stem cell / cardiomyocyte / data / machine learning / regression / descriptors / iPS / cardiomyocite / statistics / small molecule / Stem cell / Cardiomyocite / Screening / Machine learning / DFT / Data-driven / ベイズ最適化 / 材料情報 / 機械学習 / 幹細胞分化増殖 / 再生医療 |
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| Outline of Research at the Start |
In order to generate tissues for clinical purposes, cells of a specific type must be created. Such cells are usually generated by adding nutrients to a stem cell culture medium. This project will develop an AI-based platform for efficient identification nutrients for inducing the target cell state.
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| Outline of Annual Research Achievements |
This project aims to predict compounds for inducing cardiac differentiation of stem cells using data science. During FY2022, we created a series of regression models which can predict whether small organic molecules can be used as WNT inhibitors in a cardiac differentiation protocol. In FY2023, we extended this work as follows: (i) minor tweaking of model performance by introducing thermodynamically averaged descriptors; (ii) development of a new random sensitivity analysis technique for assessing model robustness to overtraining; (iii) prediction of several new compounds for use as WNT inhibitors; (iv) synthesis and experimental verification of one of these compounds when used in a real iPS stem cell differentiation protocol.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The project was delayed during FY2020 and FY2021 due to pandemic-related travel barriers, as well as the loss of a stem cell laboratory at our institute, which prevented us from acquiring data early in the project. However, after acquiring an existing data set in early FY2021 we could swiftly progress through the research plan, meeting all of the research goals at roughly the targeted times.
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| Strategy for Future Research Activity |
The project has been extended by one year due to a large amount of unused budget from FY2023 (around 1M yen). The additional year will be used write a paper and attend international conferences to share the results.
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Report
(4 results)
Research Products
(12 results)
