ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2009
Drosophila Gene Expression Pattern Annotation Using Sparse Features and Term-Term Interactions.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 38 citations in OpenAlex.
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- Image-level and group-level models for Drosophila gene expression pattern annotation.BMC bioinformatics · 2013Article
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- Joint stage recognition and anatomical annotation of Drosophila gene expression patterns.Bioinformatics (Oxford, England) · 2012Article
- Learning sparse representations for fruit-fly gene expression pattern image annotation and retrieval.BMC bioinformatics · 2012Article
- Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks.ACM transactions on knowledge discovery from data · 2012Article
- SPEX2: automated concise extraction of spatial gene expression patterns from Fly embryo ISH images.Bioinformatics (Oxford, England) · 2010Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.