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Updated: Feb 18, 2026

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers
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Published on: July 18, 2025

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EPINTLM:前訓練された k-mer 埋め込みと残留クロスアテネションによるエンハンサー・プロモーター予測

Thi Lan Nguyen1,2, Hien Quang Kha2,3, Phat Ky Nguyen2,3

  • 1VNU University of Engineering and Technology, Vietnam National University, 144 Xuan Thuy, Cau Giay, Hanoi 100000, Vietnam.

Briefings in bioinformatics
|February 16, 2026
PubMed
まとめ

ディープラーニングモデルであるEPINTLMは,DNA配列とゲノム特性を統合することによって,エンハンサー・プロモーター相互作用 (EPI) を正確に予測します. このフレームワークは,改善された計算効率と再現性により,遺伝子調節研究を強化します.

キーワード:
クロス・アテンション・メカニズムenhancer 強化者 促進者 相互作用ゲノムシーケンスゲノムシーケンスマルチモダルの特性が特徴です.前もって訓練された大型言語モデル.残りの接続は残留接続である.

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