Structured Learning in Biological Domain
Structured Learning in Biological Domain
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DOI：
10.1007/s11518-020-5461-5
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发表时间：
2020-07
影响因子：
1.2
通讯作者：
Canh Hao Nguyen
中科院分区：
文献类型：
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作者：
Canh Hao Nguyen
生物学领域拥有越来越多的生物技术数据和数据集成工具。在机器学习和人工智能的复兴中，数据驱动的生物知识发现有很大的希望。然而，由于隐藏在数据中的领域知识的复杂性，它并不是直接的。在任何层面上，无论是原子、分子、细胞还是生物体，生物成分之间都存在着丰富的相互依赖性。这一领域的机器学习方法通常涉及分析编码在图和相关形式主义中的相互依赖结构。在本报告中，我们回顾了我们为这些应用开发新的机器学习方法的工作，与最先进的方法相比，这些方法具有更好的性能。我们展示了如何使用生物成分之间的网络来预测属性。
Biological domain has been blessed with more and more data from biotechnologies as well as data integration tools. In the renaissance of machine learning and artificial intelligence, there is so much promise of data-driven biological knowledge discovery. However, it is not straight forward due to the complexity of the domain knowledge hidden in the data. At any level, be it atoms, molecules, cells or organisms, there are rich interdependencies among biological components. Machine learning approaches in this domain usually involves analyzing interdependency structures encoded in graphs and related formalisms. In this report, we review our work in developing new Machine Learning methods for these applications with improved performances in comparison with state-of-the-art methods. We show how the networks among biological components can be used to predict properties.