2009 DrosophilaGeneExpressionPattern

• (Ji et al., 2009) ⇒ Shuiwang Ji, Lei Yuan, Ying-Xin Li, Zhi-Hua Zhou, Sudhir Kumar, and Jieping Ye. (2009). “Drosophila Gene Expression Pattern Annotation Using Sparse Features and Term-term Interactions.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557068

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Drosophila+gene+expression+pattern+annotation+using+sparse+features+and+term-term+interactions%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557068&preflayout=flat#citedby

Quotes

Author Keywords

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

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 DrosophilaGeneExpressionPattern	Shuiwang Ji Lei Yuan Ying-Xin Li Zhi-Hua Zhou Sudhir Kumar Jieping Ye			Drosophila Gene Expression Pattern Annotation Using Sparse Features and Term-term Interactions		KDD-2009 Proceedings		10.1145/1557019.1557068		2009