ダウンロード数: 34
このアイテムのファイル:
ファイル | 記述 | サイズ | フォーマット | |
---|---|---|---|---|
s41540-023-00321-5.pdf | 1.8 MB | Adobe PDF | 見る/開く |
タイトル: | Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates |
著者: | Yada, Yuichiro Honda, Naoki |
著者名の別形: | 矢田, 祐一郎 本田, 直樹 |
キーワード: | Bayesian inference Biomarkers Stochastic modelling |
発行日: | 23-Nov-2023 |
出版者: | Springer Nature |
誌名: | npj Systems Biology and Applications |
巻: | 9 |
論文番号: | 59 |
抄録: | The pair-wise observation of the input and target values obtained from the same sample is mandatory in any prediction problem. In the biomarker discovery of Alzheimer’s disease (AD), however, obtaining such paired data is laborious and often avoided. Accumulation of amyloid-beta (Aβ) in the brain precedes neurodegeneration in AD, and the quantitative accumulation level may reflect disease progression in the very early phase. Nevertheless, the direct observation of Aβ is rarely paired with the observation of other biomarker candidates. To this end, we established a method that quantitatively predicts Aβ accumulation from biomarker candidates by integrating the mostly unpaired observations via a few-shot learning approach. When applied to 5xFAD mouse behavioral data, the proposed method predicted the accumulation level that conformed to the observed amount of Aβ in the samples with paired data. The results suggest that the proposed model can contribute to discovering Aβ predictability-based biomarkers. |
記述: | アルツハイマー病の予兆候補の発見に役立つ機械学習モデル開発 --現実的な実験データの制約下で適用可能なモデル--. 京都大学プレスリリース. 2023-12-26. |
著作権等: | © The Author(s) 2023 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
URI: | http://hdl.handle.net/2433/286505 |
DOI(出版社版): | 10.1038/s41540-023-00321-5 |
PubMed ID: | 37993458 |
関連リンク: | https://www.kyoto-u.ac.jp/ja/research-news/2023-12-26 |
出現コレクション: | 学術雑誌掲載論文等 |
![](/dspace/image/articlelinker.gif)
このアイテムは次のライセンスが設定されています: クリエイティブ・コモンズ・ライセンス