2009 DrosophilaGeneExpressionPattern

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Gene Expression Pattern, Image Annotation, Bag-of-words, Sparse Learning, Regularization


The Drosophila gene expression pattern images document the spatial and temporal dynamics of gene expression and they are valuable tools for explicating the gene functions, interaction, and networks during Drosophila embryogenesis. To provide text-based pattern searching, the images in the Berkeley Drosophila Genome Project (BDGP) study are annotated with ontology terms manually by human curators. We present a systematic approach for automating this task, because the number of images needing text descriptions is now rapidly increasing. We consider both improved feature representation and novel learning formulation to boost the annotation performance. For feature representation, we adapt the bag-of-words scheme commonly used in visual recognition problems so that the image group information in the BDGP study is retained. Moreover, images from multiple views can be integrated naturally in this representation. To reduce the quantization error caused by the bag-of-words representation, we propose an improved feature representation scheme based on the sparse learning technique. In the design of learning formulation, we propose a local regularization framework that can incorporate the correlations among terms explicitly. We further show that the resulting optimization problem admits an analytical solution. Experimental results show that the representation based on sparse learning outperforms the bag-of-words representation significantly. Results also show that incorporation of the term-term correlations improves the annotation performance consistently.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2009 DrosophilaGeneExpressionPatternShuiwang Ji
Lei Yuan
Ying-Xin Li
Zhi-Hua Zhou
Sudhir Kumar
Jieping Ye
Drosophila Gene Expression Pattern Annotation Using Sparse Features and Term-term InteractionsKDD-2009 Proceedings10.1145/1557019.15570682009