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相关概念视频

Statgraphics01:10

Statgraphics

119
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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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Fast Fourier Transform01:10

Fast Fourier Transform

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The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
286
Introduction to R01:11

Introduction to R

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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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Manipulation and Analysis01:21

Manipulation and Analysis

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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计算命中探测:一个行业的视角

Paraskevi Gkeka1, Fredrik Svensson2, Carlos Roca Magadán3

  • 1Integrated Drug Discovery, Molecular Design Sciences, Sanofi, Vitry-sur-Seine 91380, France.

Journal of medicinal chemistry
|May 20, 2025
PubMed
概括
此摘要是机器生成的。

包括虚拟选在内的计算匹配发现正在迅速发展. 超大型图书馆和人工智能等新技术正在改变药物发现工作流程,提供更高效和更具成本效益的方法.

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科学领域:

  • 药物发现 药物发现 药物发现
  • 计算化学计算化学

背景情况:

  • 虚拟查是药物发现的关键计算方法,补充实验方法.
  • 由于成熟的技术和有限的图书馆规模,虚拟选方面的创新已经放缓.
  • 最近计算能力和人工智能的进步正在推动该领域的重大变化.

研究的目的:

  • 提供行业专家的指南,以了解计算命中发现的不断变化的格局.
  • 为开发有效的虚拟查工作流提供实际建议.
  • 讨论风险减轻策略,成功标准和药物发现新兴技术.

主要方法:

  • 总结了不断变化的计算命中搜索格局的关键方面.
  • 为构建端到端选工作流程提供实际建议.
  • 讨论降低风险的策略,并确定成功标准.

主要成果:

  • 计算命中探测领域正在经历重大转型.
  • 像超大型虚拟图书馆和人工智能这样的新兴技术正在增强选能力.
  • 行业从业者提供了对优化工作流程和应对挑战的见解.

结论:

  • 先进的计算工具的整合正在彻底改变药物发现管道.
  • 战略性实施新技术可以提高发现命中者的效率和成功.
  • 持续适应新兴技术对于未来的药物发现工作至关重要.