相关实验视频

Updated: Jun 29, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

一个改进的指标和基准来评估虚拟选模型的性能

Michael Brocidiacono¹, Konstantin I Popov¹, Alexander Tropsha¹

¹Univerity of North Carolina at Chapel Hill.

|April 1, 2024

概括

大型图书馆的基于结构的虚拟选 (SBVS) 性能很难评估. 引入了一个新的基准,BayesBind和一个改进的丰富公式,以更好地评估计算药物发现模型.

科学领域:

计算机化药物发现.
化学信息学化学信息学
生物信息学是一种生物信息学.

背景情况:

基于结构的虚拟查 (SBVS) 对于识别候选药物至关重要.
现有的丰富公式与大型化学图书馆和机器学习集成作斗争.
当前基准风险数据泄露,阻碍准确的模型评估.

结论:

拟议的丰富公式和BayesBind基准提供了对SBVS模型的更强有力的评估.
贝叶斯Bind促进了机器学习模型在药物发现中的可靠评估.
贝叶斯Bind的公开发布旨在推动计算药物发现领域的发展.

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Testing a Claim about Standard Deviation

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...