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

Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning,10.1109/TCBB.2011.73,IEEE/ACM Transactions on Computational

Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning   (Citations: 7)
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The Berkeley Drosophila Genome Project (BDGP) has produced a large number of gene expression patterns, many of which have been annotated tex- tually 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 devel- opment of the computational method to automate the textual description of expression patterns con- tained 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 prob- lems that beset the annotation task. Empirical study shows that the proposed method outperforms the state-of-the-art Drosophila gene expression pattern annotation methods.
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