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Updated: Feb 14, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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人工知能とマニュアルキュレーションを統合して,ChEMBLのバイオアッセイアノテーションを強化する

Ines Smit1, Melissa F Adasme1, Emma Manners1

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB101SD, UK.

Journal of cheminformatics
|February 12, 2026
PubMed
まとめ

ChEMBLのバイオアッセイデータ品質の向上は,化学情報学と機械学習 (ML) にとって極めて重要です. 最近の取り組みは,アッセイメタデータの標準化と機械可読性を改善し,堅固な複合標的モデリングのためのFAIRデータ原理を推進しています.

キーワード:
バイオアッセイ・オントロジーバイオアッセイ アノテーションチェンブル チェンブル実験方法の実験方法についてFAIR データはフェアです.機械学習 (Machine Learning) とは,機械学習 (Machine Learning) というものです.命名されたエンティティの認識自然言語処理は自然言語の処理である.

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