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強化剤-促進剤の相互作用を特定するための計算方法.

Haiyan Gong1, Zhengyuan Chen1, Yuxin Tang1

  • 1School of Computer and Communication Engineering Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing 100083 China.

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まとめ
この要約は機械生成です。

このレビューでは,遺伝子調節において極めて重要なエンハンサー・プロモーター相互作用 (EPI) の識別方法についてまとめています. ディープラーニングの進歩を強調し,これらのゲノム要素と癌などの疾患におけるその役割を研究する研究者のためのフレームワークを提供します.

キーワード:
ディープラーニングとは,ディープラーニングです.強化剤は,強化剤を強化するものです.強化剤と促進剤の相互作用です.機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) と呼ばれるものです.プロモーターはプロモーターです.

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科学分野:

  • ゲノミクスゲノミクスとは
  • コンピュータ生物学 コンピュータ生物学
  • 分子生物学は分子生物学である.

背景:

  • エンハンサー・プロモーター相互作用 (EPI) は,ヒトゲノムのシス調節機構の重要な構成要素です.
  • EPIを特定することは,遺伝子発現の調節を理解するために不可欠です.
  • EPIを検出するための現在の方法は,研究者が適用と最適化を支援するために,体系的なレビューを必要とします.

研究 の 目的:

  • 強化剤-促進剤相互作用 (EPI) を特定するための方法の包括的なレビューを提供すること.
  • EPIを予測するための枠組みを記述し,利用可能なデータセットと予測ツールを要約します.
  • EPIの識別方法の適用を病気の文脈,特にがんの文脈で検討する.

主な方法:

  • 2010年以来,EPIの識別のためのシーケンシング技術とコンピューティングモデルの体系的なレビュー.
  • データの特徴 (遺伝子,ゲノム,エピゲノム) に基づく予測方法の分類.
  • EPI予測のための転送学習を含む,機械学習とディープラーニングのアプローチの評価.

主要な成果:

  • EPIは遺伝子発現を調節する上で重要な役割を果たします.
  • 強化剤,促進剤,およびそれらの相互作用を予測するために,ディープラーニングモデルを含む多数の計算方法が開発されています.
  • 関連するデータセットとツールにアクセスするためのウェブサイトを要約しています.
  • EPIの識別方法は,がんなどの病気の研究にますます適用されています.

結論:

  • コンピュータ技術の進歩,特にディープラーニングとトランスファーラーニングは,さまざまなゲノム特征からEPIの正確な予測を可能にします.
  • ディープラーニングモデルは,DNA配列からEPIを直接予測することができ,研究者の計算時間を短縮します.
  • このレビューは,エンハンサー・プロモーター相互作用研究の分野に入ろうとしている科学者のための詳細な研究枠組みを提供します.