全血遺伝子発現プロファイリングと20の機械学習アルゴリズムに基づく静脈血栓塞栓症リスク予測モデルの開発：包括的分析研究
Yedong Huang1, Xiaoyun Chen2, Guannan Bai3
1Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
JMIR medical informatics
|January 16, 2026
まとめ
本研究では、遺伝子発現データを用いた静脈血栓塞栓症（VTE）リスク予測のための効果的な機械学習モデル9件を開発した。これらのモデルは、Dダイマー検査と組み合わせることでVTE診断を改善する可能性を示している。
科学分野:
- ゲノム科学
- バイオインフォマティクス
- 機械学習
背景:
- 静脈血栓塞栓症（VTE）には、遺伝子発現データを利用した堅牢なリスク予測モデルが欠如しています。
- 現在のVTE診断法は、予測精度と効率の向上が必要です。
主な方法:
- トレーニングと検証のために、Gene Expression Omnibus（GEO）データベースから2つのトランスクリプトームデータセットを利用しました。
- LASSO、Random Forest、Recursive Feature Eliminationを含む特徴選択技術を採用しました。
- ROC曲線と混同行列を用いて性能を評価し、20の機械学習モデルを構築・評価しました。
結論:
- 遺伝子発現に基づいたVTE予測のための有意な診断性能を持つ9件の機械学習モデルを開発しました。
- これらの遺伝子発現ベースのモデルは、Dダイマーと組み合わせることで、VTE診断のための貴重な新しいツールを提供する可能性があります。
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