Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning.

Ying-Xin Li, Shuiwang Ji, Sudhir Kumar, Jieping Ye, Zhi-Hua Zhou
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Abstract

The Berkeley Drosophila Genome Project (BDGP) has produced a large number of gene expression patterns, many of which have been annotated textually with anatomical and developmental terms. These terms spatially correspond to local regions of the images; however, they are attached collectively to groups of images, such that it is unknown which term is assigned to which region of which image in the group. This poses a challenge to the development of the computational method to automate the textual description of expression patterns contained in each image. In this paper, we show that the underlying nature of this task matches well with a new machine learning framework, Multi-Instance Multi-Label learning (MIML). We propose a new MIML support vector machine to solve the problems that beset the annotation task. Empirical study shows that the proposed method outperforms the state-of-the-art Drosophila gene expression pattern annotation methods.

基于多实例多标签学习的果蝇基因表达模式标注。
伯克利果蝇基因组计划(BDGP)已经产生了大量的基因表达模式,其中许多已经用解剖学和发育术语进行了文本注释。这些项在空间上对应于图像的局部区域;然而,它们被集体地附加到一组图像上,因此不知道哪个术语被分配给组中哪个图像的哪个区域。这对计算方法的发展提出了挑战,以自动描述每个图像中包含的表达模式的文本。在本文中,我们证明了该任务的基本性质与一种新的机器学习框架——多实例多标签学习(MIML)很好地匹配。我们提出了一种新的MIML支持向量机来解决困扰标注任务的问题。实证研究表明,该方法优于目前最先进的果蝇基因表达模式注释方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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