TB-ML-a framework for comparing machine learning approaches to predict drug resistance of Mycobacterium tuberculosis.
TB-ML-a framework for comparing machine learning approaches to predict drug resistance of Mycobacterium tuberculosis.
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DOI：
10.1093/bioadv/vbad040
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发表时间：
2023
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机器学习(ML)在从序列数据预测抗菌素耐药性(AMR)方面表现出令人印象深刻的性能，包括结核病的病原体结核分枝杆菌。然而，目前的ML开发和出版实践使研究人员和临床医生很难使用、测试或复制已发表的模型。我们将一些已发表和未发表的用于预测结核分枝杆菌AMR的ML模型打包到Docker容器中。同样，将基因组数据预处理为模型所需格式所需的管道也被打包到单独的容器中。通过遵循最小的容器I/O标准，我们确保了尽可能多的互操作性。我们还创建了一个命令行应用程序TB-ML，它可以用来轻松地将预处理和预测容器组合成完整的管道，准备通过一个命令从新的原始数据预测阻力。只要遵守容器接口的最低标准，持有新型号的研究人员生产的容器同样可以包括在这些管道中，使不同型号的基准比较变得简单，并有助于在临床上更快地采用。TB-ML包含一个用Python语言编写的简单Docker API，可从https://github.com/jodyphelan/tb-ml.获得Https://tb-ml.github.io/tb-ml-containers/.上提供了用于阻力预测和相应数据预处理的Docker容器示例以及有关如何为TB-ML创建新容器的教程邮箱：jody.phelan@lshtm.ac.uk
Machine learning (ML) has shown impressive performance in predicting antimicrobial resistance (AMR) from sequence data, including for Mycobacterium tuberculosis, the causative agent of tuberculosis. However, current ML development and publication practices make it difficult for researchers and clinicians to use, test or reproduce published models. We packaged a number of published and unpublished ML models for predicting AMR of M.tuberculosis into Docker containers. Similarly, the pipelines required for pre-processing genomic data into the formats required by the models were also packaged into separate containers. By following a minimal container I/O standard, we ensured as much interoperability as possible. We also created a command-line application, TB-ML, which can be used to easily combine pre-processing and prediction containers into complete pipelines ready for predicting resistance from novel, raw data with a single command. As long as there is adherence to this minimal standard for the container interface, containers produced by researchers holding new models can likewise be included in these pipelines, making benchmark comparisons of different models simple and facilitating faster uptake in the clinic. TB-ML contains a simple Docker API written in Python and is available at https://github.com/jodyphelan/tb-ml. Example Docker containers for resistance prediction and corresponding data pre-processing as well as a tutorial on how to create new containers for TB-ML are available at https://tb-ml.github.io/tb-ml-containers/. jody.phelan@lshtm.ac.uk
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影响因子：
14.9
作者：
Kim S;Thiessen PA;Bolton EE;Chen J;Fu G;Gindulyte A;Han L;He J;He S;Shoemaker BA;Wang J;Yu B;Zhang J;Bryant SH
通讯作者：
Bryant SH
DOI：
10.1136/amiajnl-2013-002512
发表时间：
2014-10-01
影响因子：
6.4
作者：
Cheng, Feixiong;Zhao, Zhongming
通讯作者：
Zhao, Zhongming
影响因子：
9.5
作者：
Lin, Shenggeng;Wang, Yanjing;Wei, Dong-Qing
通讯作者：
Wei, Dong-Qing
DOI：
10.1186/2193-9616-1-17
发表时间：
2013
期刊：
In silico pharmacology
影响因子：
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作者：
Masoudi-Nejad A;Mousavian Z;Bozorgmehr JH
通讯作者：
Bozorgmehr JH
影响因子：
8.6
作者：
通讯作者：
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