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相关概念视频

Tumor Progression02:07

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
152
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
356
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
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相关实验视频

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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使用高斯过程揭示慢性疾病进展模式,用于阶段推断.

Yanfei Wang1, Weiling Zhao1, Angela Ross1

  • 1Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States.

Journal of the American Medical Informatics Association : JAMIA
|December 6, 2023
PubMed
概括

阶段推理高斯过程 (GPSI) 揭示慢性疾病模式,识别患者子组以进行定制治疗. 这种方法有助于了解疾病进展和异质性,以改善临床实践和药物开发.

关键词:
斯过程是高斯过程.疾病的进展 疾病的进展没有监督的学习学习.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物统计学 生物统计学
  • 医疗信息学医学信息学

背景情况:

  • 慢性疾病往往进展缓慢,早期症状微妙,难以建模.
  • 疾病异质性和离散的患者观察使理解疾病进展变得复杂.
  • 准确的疾病建模影响临床实践和药物开发.

研究的目的:

  • 开发一种新的方法来发现慢性疾病的进展模式.
  • 评估临床特征对疾病进展的动态贡献.
  • 为了对骨关节炎,双相情感障碍和肝细胞癌的患者数据进行分层.

主要方法:

  • 开发了高斯过程阶段推理 (GPSI) 方法.
  • 将GPSI应用于合成和真实世界的数据集 (OA,BP,HCC).
  • 利用无监督学习来解开时间和表型异质性的纠.

主要成果:

  • GPSI确定了与基因型和途径相关的独特的OA子组.
  • GPSI揭示了两种BP发育模式和大脑缩贡献.
  • 在独立的数据集中,HCC进展模式被一致地复制.

结论:

  • 无监督的方法可以识别具有共同进展模式的疾病亚组.
  • GPSI有效地解开了疾病的异质性.
  • 这些发现使得基于疾病阶段和特征的个性化治疗计划成为可能.