Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
复制标题
通过多实例多标签学习注释果蝇基因表达模式
DOI：
10.1109/tcbb.2011.73
复制
发表时间：
2012-01-01
影响因子：
4.5
通讯作者：
Zhou, Zhi-Hua
中科院分区：
文献类型：
--
作者：
Li, Ying-Xin;Ji, Shuiwang;Zhou, Zhi-Hua
在果蝇胚胎发育的研究中，大量的基因表达模式的二维数字图像已经产生，以建立跨发育时间的时空基因表达动态图谱。在这些图像中捕获的基因表达已经使用受控词汇表（CV）用解剖学和发育本体论术语手动注释，这在旨在理解基因功能，相互作用和网络的研究中非常有用。随着图像的快速积累，人工标注的过程变得越来越繁琐，迫切需要自动化这项任务的计算方法。然而，胚胎图像的自动注释是具有挑战性的。这是因为注释项在空间上对应于图像的局部表达模式，但是它们被共同分配给图像组，并且不知道哪个项对应于组中哪个图像的哪个区域。在本文中，我们使用一种新的机器学习框架，多实例多标签（MIML）学习来解决这个问题。我们首先表明，注释任务的基本性质是一个典型的MIML学习问题。然后，我们提出了两个支持向量机算法的MIML框架下的任务。在FlyExpress数据库（一个标准化果蝇基因表达模式图像的数字库）上的实验结果表明，MIML框架的开发与现有技术相比，性能有了显著的提高。
In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time. Gene expressions captured in these images have been manually annotated with anatomical and developmental ontology terms using a controlled vocabulary (CV), which are useful in research aimed at understanding gene functions, interactions, and networks. With the rapid accumulation of images, the process of manual annotation has become increasingly cumbersome, and computational methods to automate this task are urgently needed. However, the automated annotation of embryo images is challenging. This is because the annotation terms spatially correspond to local expression patterns of images, yet they are assigned collectively to groups of images and it is unknown which term corresponds to which region of which image in the group. In this paper, we address this problem using a new machine learning framework, Multi-Instance Multi-Label (MIML) learning. We first show that the underlying nature of the annotation task is a typical MIML learning problem. Then, we propose two support vector machine algorithms under the MIML framework for the task. Experimental results on the FlyExpress database (a digital library of standardized Drosophila gene expression pattern images) reveal that the exploitation of MIML framework leads to significant performance improvement over state-of-the-art approaches.