解釈可能な機械学習モデルによる筋肉発達の遺伝子発現と代替スプライシングの基礎を解読する
Xiaodong Tan1,2, Minjie Huang1,2, Yuting Jin1,2
1Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
Biology
|September 4, 2025
まとめ
この研究では 機械学習を用いて 鶏の胸の筋肉の生産量に関連した 重要な遺伝子と 接合現象を特定しました AIモデルは筋肉の割合を正確に予測し 鶏の繁殖戦略を助けます
科学分野:
- ゲノミクス
- バイオ情報学
- 動物の繁殖
背景:
- 肉の収穫は家禽の繁殖プログラムにとって非常に重要です
- 筋肉の生産性の 分子マーカーを特定することは 遺伝子の改善に不可欠です
研究 の 目的:
- トランスクリプトームシーケンシングと機械学習を使用して,鶏の胸筋重量パーセント (BrP) の分子マーカーを特定します.
- 遺伝子発現と代替スプライシングデータを用いて,BrPの正確な予測モデルを開発する.
主な方法:
- トランスクリプトームシーケンス ブライラーと地元の鶏の筋肉組織
- 差別遺伝子発現 (DEG) と代替スプライシング (AS) 分析
- 機械学習モデル開発 (XGBoost,Glmnetなど) と機能重要性評価 (SHAP)
主要な成果:
- BrPと有意に関連した50のDEGと95の差異的にスプライスされたトランスクリプト (DST) を特定した.
- DEG (XGBoost) を使用した BrP 予測の精度は>90%で,DST (Glmnet) を使用した 95%に達しました.
- BrPに寄与する重要な遺伝子 (例えば,ENSGALG00012060,HINTW,VIPR2-201) とASイベント
結論:
- 鶏の乳腺の筋肉の特性を予測する高精度AIモデルを開発しました.
- 分子繁殖のための新しい候補遺伝子とAS標的を特定した.
- 経済的に重要な特徴の遺伝子マーカーを特定する上で AIへの移行を示した.
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