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Aggregates Classification01:29

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Understanding how a drug's concentration fluctuates within the body over time is crucial in pharmacokinetics, particularly with multiple oral doses. A graphical representation of multiple oral dosages provides insight into these dynamics. Typical accumulation curves of a drug's concentration in the body reveal a sawtooth pattern, indicating periodic peaks and troughs correlating with each dose administration and the drug's subsequent elimination.The plasma concentration at any time during an...
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Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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PGVDA：機械学習を用いた解釈可能な疾患分類のための経路集約型遺伝子量フレームワーク

Sanghyun Shon1, Younhee Ko2, Hojin Yoon1,3

  • 1Department of Biomedical Informatics, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.

Briefings in bioinformatics
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PubMed
まとめ
この要約は機械生成です。

本研究では、経路ベースの遺伝子バリアント量平均（PGVDA）を用いた機械学習アプローチを導入し、生物学的経路内の遺伝子バリアントを解析することで、神経筋接合部障害（NMD）と炎症性多発ニューロパチー（IPN）を区別する。

キーワード:
遺伝子差炎症性多発ニューロパチー機械学習分類神経筋疾患経路ベース集約SHAP分析

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科学分野:

  • 遺伝学
  • 神経学
  • 計算生物学

背景:

  • 神経筋接合部障害（NMD）と炎症性多発ニューロパチー（IPN）は異なりますが、生物学的経路を共有しています。
  • 限定的な遺伝子比較しか存在せず、根本的な違いの理解を妨げています。
  • 機械学習（ML）は、バリアントパターンに基づいてこれらの疾患を区別する可能性を提供します。

主な方法:

  • 667人のUKバイオバンク参加者からの非同義バリアントを利用しました。
  • バリアント関連付けと経路濃縮分析にロジスティック回帰を使用しました。
  • 経路内のバリアント量の対数オッズ比を平均することによってPGVDAを開発しました。
  • MLモデル評価のために次元削減と1つ除外交差検証を適用しました。
  • 経路レベルとバリアントレベルの洞察のためにSHAP値を使用して結果を解釈しました。

結論:

  • PGVDAを用いた経路レベルの遺伝子バリアント解析は、NMDとIPNを区別するための正確で解釈可能な方法を提供します。
  • このアプローチは、これらの神経筋状態を区別するために重要な特定の遺伝子と経路を強調します。
  • 一般化を確認するために、さらなる外部検証が推奨されます。