スケール可能で解釈可能なガウスのプロセスによる学習配列関数関係
bioRxiv : the preprint server for biology
|September 2, 2025
まとめ
ゲノタイプ-フェノタイプ関係を理解するために 解釈可能なガウスのプロセスモデルを開発しました 大量の生物学的配列データセットにおけるエピスタシスを説明します 私たちのアプローチは 優れた予測能力を提供し 新しい遺伝子の相互作用を 明らかにします
科学分野:
- 遺伝学とバイオ情報学
- コンピュータ生物学
- システム生物学
背景:
- ゲノタイプとフェノタイプの関係を理解することは遺伝学において極めて重要であるが,エピスタシス (文脈に依存する変異的効果) によって複雑である.
- ハイ・スループット・フェノタイプ化は大きなデータセットを生成しますが,標準モデルは一般化性と解釈性に問題があります.
- ディープニューラルネットワークは 柔軟性がありますが 解釈性や不確実性の量化には欠けています
研究 の 目的:
- 配列関数関係の解釈可能なガウスのプロセスモデルの新しいファミリーを導入する.
- フィットネス・ランドスケープモデルを一般化する柔軟な先行分布を用いてエピスタシスを捉える.
- 複雑な遺伝子の相互作用を探求するためのスケーラブルで解釈可能な方法を提供する.
主な方法:
- モデルエピスタシスに柔軟な先行分布を持つ解釈可能なガウスのプロセスモデルを開発した.
- エピスタティック効果を定量化するために,サイト,アレル,および変異特有の要因を組み込む.
- 大量のデータセット (タンパク質,RNA,全ゲノムSNP) へのスケーラビリティのためのGPU加速を使用した.
主要な成果:
- 大きな生物学的配列データセットで優れた予測性能を達成しました.
- 既知の遺伝的特徴を復元する解釈可能なモデルパラメータを生成します.
- ゲノタイプ-フェノタイプマップに関する新しい洞察を提供した.
結論:
- 開発されたガウスのプロセスモデルは,配列関数関係を研究するためのスケーラブルで解釈可能なアプローチを提供します.
- これらのモデルはエピスタシスを効果的に捕捉し,遺伝子型-フェノタイプマップのより深い洞察を提供します.
- この方法は,DNA,RNA,タンパク質の配列を含む多様な生物系に適用できます.
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