ATPDraw用户手册:入门与安装指南

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"非线性基本元件-an introduction to bioinformatics algorithms" 该资源似乎是一个关于生物信息学算法的介绍,但提供的信息并不直接与这个主题相关。相反,提供的内容是一本名为"ATPDraw用户手册"的手册片段,这是一款用于电路设计和分析的软件的用户指南。ATPDraw支持非线性基本元件的绘制和处理,可能是模拟电子电路的一部分,而这些元件在生物信息学中可能并不常见。然而,由于信息不足,我们无法深入探讨非线性元件在生物信息学中的应用。 ATPDraw是Advanced Technology Programmer(ATP)的一个配套工具,它允许用户创建、编辑和管理.CIR格式的电路文件。手册涵盖了从软件的简介、发展历史、支持的元件类型到安装和配置的详细步骤。此外,手册还教导用户如何转换旧版本的.CIR文件,获取帮助,以及利用ATPDraw运行相关应用程序,如ATP(Advanced Technology Program),TPPLOT,PCPLOT,PlotXY和LCC等。 在入门部分,手册介绍了ATPDraw的基本界面和操作,包括窗口操作、主窗口的使用、元件对话框、鼠标操作、编辑功能,以及如何进行电路编辑。特别地,它提供了一个简单的电路构建教程(Exa_1.cir),指导新手从创建新电路、添加电源、二极管桥、负载到定义节点名称和接地的基本步骤。 通过电子邮件、FTP服务器和万维网,手册还列出了获取ATP相关网络资源的方法,这对于用户获取更新、技术支持和进一步学习是非常有用的。 虽然标题提及的是生物信息学算法,但实际内容涉及的是电路设计软件ATPDraw的使用,特别是对于非线性元件的操作。这个手册是针对那些想要使用ATPDraw进行电路设计和分析的电子工程师或相关领域学生的重要参考资料。

精简下面表达:Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

2023-02-27 上传