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Assessment:
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Characteristics of Practical Op Amps01:16

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A difference amplifier, a crucial component in numerous electronic devices, ideally amplifies only the difference-mode signal, which is the difference between two input signals. However, in practical circuits, the output voltage depends on both the differential gain and the common-mode gain.
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BioMEMS: Forging New Collaborations Between Biologists and Engineers
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民間および実用的な薬学的な協力の実現

Brian Hie1, Hyunghoon Cho1, Bonnie Berger2,3

  • 1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.

Science (New York, N.Y.)
|October 20, 2018
PubMed
まとめ
この要約は機械生成です。

この研究では,薬剤標的相互作用 (DTI) モデルのトレーニングのための安全な計算プロトコルが導入され,プライバシーを保護し,新しいDTIの発見を検証しながらデータ共有を可能にします.

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科学分野:

  • 計算生物学
  • 薬理学について
  • 暗号化

背景:

  • 薬物発見のための薬理学的データの共有は,プライバシーと知的財産に関する懸念によって妨げられています.
  • 薬物標的相互作用 (DTI) を予測する既存の方法は,しばしば集中的なデータにアクセスする必要があります.
  • 生命を救う突破は 繊細な薬理学的データセットを 組み合わせることのできないことによって制限されています

研究 の 目的:

  • プーリングされた薬理学的データで予測モデルを訓練するための安全な計算プロトコルを開発する.
  • 薬物,標的,相互作用の機密性を確保することでデータ共有の障壁を克服する.
  • データのプライバシーを損なうことなく,正確なDTI予測と新しい相互作用の発見を可能にします.

主な方法:

  • 近代的な暗号化ツールを活用して 安全な計算プロトコルを作成します
  • 大規模なデータセット (> 100万件の相互作用) で薬物標的相互作用 (DTI) の予測モデルを訓練する.
  • 裏付け可能な機密性メカニズムの利用

主要な成果:

  • このプロトコルは,実際のデータセットで数日でDTI予測モデルを成功裏に訓練しました.
  • 開発されたプロトコルは,既存の最先端のDTI予測方法よりも高い精度を達成しました.
  • このプロトコルを用いて新しいDTIが発見され,標的型アッセイによって実験的に検証された.

結論:

  • 開発された暗号プロトコルは,プールされたデータに関するDTIモデルの安全で効果的なトレーニングを可能にします.
  • このアプローチは,薬理学研究におけるデータ共有の重要な障壁を克服します.
  • 検証された発見と方法論は 強化された共同生物医学研究のための基礎を築く.