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関連する概念動画

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

503
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
503
Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Peptide Bonds02:43

Peptide Bonds

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A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
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Bacterial Transformation01:33

Bacterial Transformation

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In 1928, bacteriologist Frederick Griffith worked on a vaccine for pneumonia, which is caused by Streptococcus pneumoniae bacteria. Griffith studied two pneumonia strains in mice: one pathogenic and one non-pathogenic. Only the pathogenic strain killed host mice.
Griffith made an unexpected discovery when he killed the pathogenic strain and mixed its remains with the live, non-pathogenic strain. Not only did the mixture kill host mice, but it also contained living pathogenic bacteria that...
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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関連する実験動画

Updated: Feb 13, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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抗がんペプチドを識別するためのディープカプセルニューラルネットワークは,シーケンスから画像変換ベースのローカルエンベデッド機能の配列を使用して,画像変換に基づいています.

Shahid Akbar1,2, Ali Raza3,4,5, Matee Ullah6,5

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China.

BMC biology
|February 12, 2026
PubMed
まとめ

新しいモデルであるpACP-CapsNetは,抗がんペプチド (ACP) を97.0%の精度で正確に識別します. このコンピューティングツールは,がん薬の開発のための有望で低副作用の代替案を提供します.

キーワード:
抗がんペプチドとはカプセルニューラルネットワークドラッグ・ディスカバリー・ドラッグ・ディスカバリーペプチド変換によるペプチド変換予測 予測 予測治療用ペプチド

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関連する実験動画

Last Updated: Feb 13, 2026

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科学分野:

  • 計算生物学とは,計算生物学である.
  • バイオインフォマティックス
  • ドラッグ・ディスカバリー・ドラッグ・ディスカバリー

背景:

  • がんは,世界的な健康上の大きな課題であり続けています.
  • 伝統的ながん治療は,高い費用と副作用のために制限に直面しています.
  • 抗がんペプチド (ACP) は,がん治療の有望な代替手段である.

研究 の 目的:

  • ACPの正確な識別のための効果的な計算モデルを開発する.
  • 抗癌ペプチド配列の予測を強化するためにディープラーニングを活用する.
  • A.C.P.の識別における既存の方法の限界に対処するために.

主な方法:

  • 入力配列は,SMRとRECMを使用して画像に変換されました.
  • 特徴抽出にはHOG,DWT,CLBPの変換が含まれ,ハイブリッドの特徴空間を作成しました.
  • 特徴の選択には,シャッフルされたカエルのジャンプアルゴリズム (SFLA) が使用されました.
  • カプセルニューラルネットワーク (CapsNet) が分類に使用されました.

主要な成果:

  • pACP-CapsNetモデルは,トレーニングデータに対して97.0%の精度と0.98のAUCを達成しました.
  • このモデルは,ACP240とACP740のテストセットで既存の方法よりも優れたパフォーマンスを示しました.
  • 統合された機能とSFLAで選択された機能により,予測率が向上しました.

結論:

  • pACP-CapsNetモデルは,ACPの識別に高い効率性と安定性を示しています.
  • このツールは,学術研究とがん薬の設計において,潜在的な応用がある.
  • このモデルは,薬物診断と新しいがん治療法の開発を容易にする.