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関連する概念動画

Polygenic Traits01:18

Polygenic Traits

69.5K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Human Genetics01:28

Human Genetics

1.7K
Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
1.7K
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

53
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...
53
Pleiotropy01:33

Pleiotropy

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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
43.6K
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

59
The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
59
Pharmacogenetics and Pharmacogenomics: Overview01:29

Pharmacogenetics and Pharmacogenomics: Overview

74
Pharmacogenetics and pharmacogenomics examine how genetic factors influence an individual's response to drugs. While pharmacogenetics focuses on the impact of specific genetic variants on drug effects, pharmacogenomics takes a broader approach, studying how genetic variation across populations contributes to differences in drug responses. These fields aim to explain why individuals may experience varying levels of efficacy or adverse reactions to the same medication.Variability in drug...
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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ポリゲニックリスクの統合による疾患進行の生成予測の改善

Chris German1, Suyash Shringarpure2, Payam Dibaeinia2

  • 123andMe, Inc., Palo Alto, CA, USA, chrisg@23andme.com.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
PubMed
まとめ
この要約は機械生成です。

新しいAIモデルであるNext Health Event（NHE）モデルは、遺伝データと健康履歴を統合して将来の病気を予測します。予測医療におけるこの進歩は、個々の疾患経路を予測するための強力な新しいフレームワークを提供します。

キーワード:
AI機械学習予測モデリング個別化医療ゲノミクス

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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科学分野:

  • 医療情報学
  • ヘルスケアにおける人工知能
  • ゲノミクスと個別化医療

背景:

  • 生涯にわたる疾患系列の予測は、医学における大きな課題です。
  • 既存のAIモデルは、コホートサイズと遺伝子データ統合の欠如によって制限されています。
  • 生成モデルは、健康軌跡予測の改善の可能性を提供します。

研究 の 目的:

  • Next Health Event（NHE）モデル、すなわち生成トランスフォーマーを導入する。
  • 人口統計データ、縦断的BMI、およびポリゲニックリスクスコア（PRS）を健康履歴に統合する。
  • 将来の疾患診断の予測精度を向上させる。

主な方法:

  • 710万人の参加者の健康軌跡でトランスフォーマーアーキテクチャをトレーニングしました。
  • 人口統計データ、縦断的BMI、および297の特性のPRSを組み込みました。
  • NHEモデルのパフォーマンスをXGBoostなどのベースラインモデルと比較しました。

主要な成果:

  • NHEモデルは、129の疾患にわたる次の診断の予測において、XGBoost（22.3%）よりも高いトップ1精度（25.5%）を達成しました。
  • ポリゲニックリスクスコアと縦断的BMIは、予測力に大きく貢献しました。
  • モデルは、将来の結果と結合された結果の報告において、一貫した精度（AUROC 0.917）を示しました。

結論:

  • NHEモデルは、大規模な健康履歴と遺伝学を統合することにより、予測医療のための新しいフレームワークを確立します。
  • 生成モデルは、個々の疾患経路を効果的に予測できます。
  • PRSと縦断的BMIは主要な予測因子ですが、ライフスタイル情報は付加価値が限定的です。