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Transcription Factors02:16

Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
Transcription Factors02:16

Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
Master Transcription Regulators02:23

Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
General Transcription Factors01:30

General Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
Master Transcription Regulators02:23

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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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トランスクリプトミクスを活用したアクティブ・ラーニング・フレームワークは,疾患のフェノタイプを調節する物質を特定する.

Benjamin DeMeo1, Charlotte Nesbitt1, Samuel A Miller1

  • 1Cellarity Inc, Somerville, MA, USA.

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PubMed
まとめ
この要約は機械生成です。

新しいディープラーニング・フレームワークは オミックスのデータを用いて 複雑な細胞現象型を誘発する薬物化合物を効率的に特定します このアプローチは 薬剤発見の成功率を大幅に高め 新しい薬の開発を加速します

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科学分野:

  • 計算生物学
  • 薬物の発見
  • ゲノミクス

背景:

  • フェノタイプ薬のスクリーニングは,化学的空間と実験的スケーラビリティによって制限されています.
  • 現在の計算方法は一般化や最適化能力が欠けていることが多い.
  • 薬の発見に用いられるゲノムプロキシはしばしばユーリスティックであり,最適化には抵抗する.

研究 の 目的:

  • 複雑な現象型を誘発する化合物を識別するためのスケーラブルで最適化可能な計算フレームワークを開発する.
  • 薬物発見のためのアクティブ・ディープ・ラーニングのアプローチの中でオミックスのデータを活用する.
  • フェノタイプ薬のスクリーニングの効率と成功率を改善する.

主な方法:

  • オミックスのデータを統合したアクティブ・ディープ・ラーニング・フレームワークを設計した.
  • 実験室内のシグネチャーの精製戦略を採用した.
  • 血液学調査のアルゴリズムを検証した

主要な成果:

  • ディープラーニング・フレームワークは,リコールにおける最先端モデルよりも優れたパフォーマンスを示しました.
  • 発見キャンペーンのフェノタイプのヒット率が 13~17倍に増加しました.
  • 実験室内の精製と組み合わせると ヒット率の2倍増加が観察され 分子洞察が得られました

結論:

  • 開発されたフレームワークは,効率的かつスケーラブルなフェノタイプのヒット識別を可能にします.
  • このアプローチは 薬剤発見のパイプラインを加速させる大きな可能性を秘めています
  • 実験的な精錬による統合は,ヒット識別をさらに強化し,メカニズム的な理解を提供します.