A bag-of-words approach for Drosophila gene expression pattern annotation
Genre
Journal ArticleDate
2009-04-21Author
Ji, SLi, YX
Zhou, ZH
Kumar, S
Ye, J
Subject
AnimalsComputational Biology
Databases, Genetic
Drosophila
Gene Expression
Gene Expression Profiling
Pattern Recognition, Automated
Permanent link to this record
http://hdl.handle.net/20.500.12613/5580
Metadata
Show full item recordDOI
10.1186/1471-2105-10-119Abstract
Background: Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task. Results: We present a computational method to annotate gene expression pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic patterns from BDGP data demonstrate that our method significantly outperforms other methods. Conclusion: The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance. © 2009 Ji et al; licensee BioMed Central Ltd.Citation to related work
Springer Science and Business Media LLCHas part
BMC BioinformaticsADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.eduae974a485f413a2113503eed53cd6c53
http://dx.doi.org/10.34944/dspace/5562