2015 AnatomicalAnnotationsforDrosoph

• (Gao et al., 2015) ⇒ Hongchang Gao, Lin Yan, Weidong Cai, and Heng Huang. (2015). “Anatomical Annotations for Drosophila Gene Expression Patterns via Multi-Dimensional Visual Descriptors Integration: Multi-Dimensional Feature Learning.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783384

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• http://scholar.google.com/scholar?q=%222015%22+Anatomical+Annotations+for+Drosophila+Gene+Expression+Patterns+via+Multi-Dimensional+Visual+Descriptors+Integration%3A+Multi-Dimensional+Feature+Learning
• http://dl.acm.org/citation.cfm?id=2783258.2783384&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; drosophila gene expression pattern annotation; multi-dimensional feature learning; multi-view data integration

Abstract

In Drosophila gene expression pattern research, the in situ hybridization (ISH) image has become the standard technique to visualize and study the spatial distribution of RNA. To facilitate the search and comparison of Drosophila gene expression patterns during Drosophila embryogenesis, it is highly desirable to annotate the tissue-level anatomical ontology terms for ISH images. In ISH image annotations, the image content representation is crucial to achieve satisfactory results. However, existing methods mainly focus on improving the classification algorithms and only using simple visual descriptor. If we integrate the effective local and holistic visual descriptors via proper learning method, we can achieve more accurate image annotation results than using individual visual descriptor.

We propose a novel structured sparsity-inducing norms based feature learning model to integrate the multi-dimensional visual descriptors for Drosophila gene expression patterns annotations. The new mixed norms are designed to learn the importance of different features from both local and global point of views. We successfully integrate six widely used visual descriptors to annotate the Drosophila gene expression patterns from the lateral, dorsal, and ventral views. The empirical results show that the proposed new method can effectively integrate different visual descriptors, and consistently outperforms related methods using the concatenated visual descriptors.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 AnatomicalAnnotationsforDrosoph	Heng Huang Hongchang Gao Lin Yan Weidong Cai			Anatomical Annotations for Drosophila Gene Expression Patterns via Multi-Dimensional Visual Descriptors Integration: Multi-Dimensional Feature Learning				10.1145/2783258.2783384		2015