用于反向传播神经网络以辅助类推的CPANNatNIC软件。
CPANNatNIC software for counter-propagation neural network to assist in read-across.
作者信息
Drgan Viktor, Župerl Špela, Vračko Marjan, Cappelli Claudia Ileana, Novič Marjana
机构信息
Department of Cheminformatics, National Institute of Chemistry, Hajdrihova 19, 1001, Ljubljana, Slovenia.
Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milan, Italy.
出版信息
J Cheminform. 2017 May 22;9(1):30. doi: 10.1186/s13321-017-0218-y.
BACKGROUND
CPANNatNIC is software for development of counter-propagation artificial neural network models. Besides the interface for training of a new neural network it also provides an interface for visualisation of the results which was developed to aid in interpretation of the results and to use the program as a tool for read-across.
RESULTS
The work presents the details of the program's interface. Parts of the interface are presented and how they can be used. The examples provided show how the user can build a new model and view the results of predictions using the interface. Examples are given to show how the software may be used in read-across.
CONCLUSIONS
CPANNatNIC provides a simple user interface for model development and visualisation. The interface implements options which may simplify read-across procedure. Statistical results show better prediction accuracy of read-across predictions than model predictions where similar compounds could be identified, which indicates the importance of using read-across and usefulness of the program.
背景
CPANNatNIC是用于开发反向传播人工神经网络模型的软件。除了用于训练新神经网络的界面外，它还提供了一个结果可视化界面，该界面旨在帮助解释结果并将该程序用作类推的工具。
结果
本文介绍了该程序界面的详细信息。展示了界面的各个部分及其使用方法。所提供的示例展示了用户如何使用该界面构建新模型并查看预测结果。还给出了示例来说明该软件如何用于类推。
结论
CPANNatNIC为模型开发和可视化提供了一个简单的用户界面。该界面实现的选项可能会简化类推过程。统计结果表明，在可以识别相似化合物的情况下，类推预测的预测准确性优于模型预测，这表明了使用类推的重要性以及该程序的实用性。
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