在Galaxy中的基于变压器的工具推系统
Anup Kumar1, Björn Grüning2, Rolf Backofen2,3
1Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110, Freiburg, Germany. kumara@informatik.uni-freiburg.de.
BMC bioinformatics
|November 27, 2023
概括
基于变压器的Galaxy新工具推系统通过提供比RNN,CNN和DNN等旧型号更快的培训,更短的使用时间和更高质量的推,显著改善了工作流扩展.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 科学工作流管理科学工作流管理
背景情况:
- 银河系是一个流行的基于网络的科学分析平台.
- 研究人员在Galaxy.com中使用了许多工具和工作流程.
- 工具推系统有助于通过建议相关工具来扩展现有分析.
研究的目的:
- 用变压器神经网络为Galaxy开发一个新的工具推系统.
- 将变压器模型的性能与循环神经网络 (RNN),卷积神经网络 (CNN) 和密集神经网络 (DNN) 的性能进行比较.
主要方法:
- 在Galaxy Europe的现有工作流中训练一个变压器神经网络.
- 评估变压器模型的融合速度,模型使用时间,概括能力和建议质量 (精度@k).
- 与RNN,CNN和DNN模型进行基准测试,以推工具.
主要成果:
- 与RNN相比,变压器模型显示了两倍更快的收速度.
- 变压器模型的使用时间 (重建和预测) 比RNN和CNN要低得多.
- 变压器实现了更高的精度@k度量 (约. 0.98) 超过了 DNN (大约. 0.9),表明了优越的推质量.
- 变压器在关键绩效指标上表现优于CNN和DNN,包括融合速度和建议准确性.
结论:
- 变压器为Galaxy建立强大的工具推系统提供了一种新且有效的方法.
- 开发的变压器模型提供了更快的培训,更低的计算开销和更高的推准确性,使研究人员在科学工作流创建和探索性数据分析中受益.
- 变压器的增强可扩展性允许对更大的数据集进行培训,为科学研究中更全面,更准确的工具建议铺平了道路.
- 推模型的开源脚本可在麻省理工学院许可证下使用.
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