プロテオミクスにおける機械学習の進展と動向 (1997年−2024年):文献測定分析
Chao Tan1, Hao Liu1, Zhen Zhang1
1Clinical Medical College & Affiliated Hospital & College of Basic Medicine, Chengdu University, Chengdu, China.
Frontiers in medicine
|September 3, 2025
まとめ
この図鑑解析は,プロテオミクスにおける機械学習 (ML) をマッピングし,指数関数的な成長とディープラーニングのような重要なトレンドを明らかにします. 未来の研究では 解釈可能なモデルと 精密医療の分野間の協力が優先されるべきです
科学分野:
- プロテオミクスとバイオインフォマティクス
- コンピュータ生物学
- 生命科学におけるデータ科学
背景:
- プロテオミクスにおける機械学習 (ML) アプリケーションへの関心が高まっている.
- ML主導のプロテオミクスの研究領域の体系的なマッピングの欠如.
- この分野における知識構造,発展軌跡,新興傾向を理解する必要がある.
研究 の 目的:
- 機械学習主導のプロテオミクスの 初めての大規模図書館解析を行うこと
- 知識の構造,発展の軌跡,そして新興の研究トレンドを明らかにする.
- 重要な研究焦点,影響力のある貢献者,協力パターンを特定する.
主な方法:
- Web of Science Core Collection の 5156 件の出版物 (1997 年から 2024 年まで) の図書館解析
- データ抽出と可視化のためにCiteSpace,VOSviewer,Scimago Graphica,R bibliometrixパッケージを使用した.
- キーワードの同時出現,引用ネットワーク,主要ジャーナル,著者,機関間の連携を分析した.
主要な成果:
- 2010年以来の指数関数的な出版増加で,2019年から2020年にかけての大幅な増加です.
- 米国は最も生産的な国であり 中国科学アカデミーは研究機関を率いています
- ディープラーニング,特にAlphaFold2は高い引用率をもたらし,主要なテーマはタンパク質とタンパク質の相互作用の予測とマルチオミクス分析である.
- コンピューター科学,分子生物学,臨床研究における 強い学際的融合
結論:
- プロテオミクスの機械学習に関する最初の包括的な文献概要を紹介しています.
- ディープラーニング,事前訓練されたモデル,マルチエコノミクスの統合.
- 精密医療の進歩のために解釈可能なモデル,強化されたコラボレーション,標準化されたデータ使用に焦点を当てることを推奨しています.
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