2018年4月2日 · 在数据科学领域，有一个经典的数据集——Pima Indians Diabetes Database，即“皮马印第安人糖尿病数据库”，它在预测疾病、机器学习模型训练等方面有着广泛的应用。

2024年6月11日 · 在数据科学领域，有一个经典的数据集——Pima Indians Diabetes Database，即“皮马印第安人糖尿病数据库”，它在预测疾病、机器学习模型训练等方面有着广泛的应用。这个数据集源于美国国立糖尿病、消化与肾脏疾病...

2025年1月6日 · Despite the relatively small size of the PIMA datasets, both k-fold and stratified k-fold cross validation are appropriate. This finding contrasts with some researchers suggesting using k-fold cross validation for class imbalance only in large datasets.

2023年12月30日 · 为了更好地理解和预测糖尿病的发病机制，我们选择了著名的Pima印第安人 糖尿病数据集，通过 数据分析 和机器学习技术，试图揭示其中隐藏的规律和特征。 我们从 UCI机器学习库 中获取了Pima印第安人糖尿病数据集，该数据集一共有768行，9列，包含了8个生理特征和一个目标变量（是否患有糖尿病）。 通过对数据的初步探索，我们获得了关于患糖尿病和非患糖尿病样本的分布比例以及各特征的基本统计信息。 通过绘制直方图和相关矩阵热力图，我们深 …

2008年11月11日 · These findings suggest that the vast majority of Pima Indians susceptible to diabetes were already developing the disease in the early years of the study, and the increasing obesity in youth combined with a nearly 4-fold increase in the frequency of exposure to diabetes in utero simply shifted the onset of diabetes to younger ages in the most ...

Let’s get some practice working with logistic regression by exploring the famous Pima diabetes data set. This data set consists of biometric data collected from women of Pima Indian descent living in Arizona, including whether or not each woman had diabetes.

2024年8月24日 · Recent work suggests that AlphaFold (AF)–a deep learning-based model that can accurately infer protein structure from sequence–may discern important features of folded protein...

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2020年7月17日 · We propose that fold-switching is an activation mechanism of PimA, and since it is a slow process, it may represent the rate-limiting step of the glycosylation reaction catalyzed by PimA. The substrate, GDP-Man, acts as an activator and, thus, functional regulator of PimA, as the binding of GDP-Man enhances the fold-switching dynamics.

AlphaFold DB provides open access to over 200 million protein structure predictions to accelerate scientific research. AlphaFold is an AI system developed by Google DeepMind that predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment.