ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Automating Gene Expression Annotation for Mouse Embryo
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Drosophila gene expression pattern annotation through multi-instance multi-label learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Information-theoretic approaches to SVM feature selection for metagenome read classification
Computational Biology and Chemistry
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Counting cells in 3D confocal images based on discriminative models
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Principles of bioimage informatics: focus on machine learning of cell patterns
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
Texture and moments-based classification of the acrosome integrity of boar spermatozoa images
Computer Methods and Programs in Biomedicine
Performance Model Selection for Learning-based Biological Image Analysis on a Cluster
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Motivation: Gene expression patterns obtained by in situ mRNA hybridization provide important information about different genes during Drosophila embryogenesis. So far, annotations of these images are done by manually assigning a subset of anatomy ontology terms to an image. This time-consuming process depends heavily on the consistency of experts. Results: We develop a system to automatically annotate a fruitfly's embryonic tissue in which a gene has expression. We formulate the task as an image pattern recognition problem. For a new fly embryo image, our system answers two questions: (1) Which stage range does an image belong to? (2) Which annotations should be assigned to an image? We propose to identify the wavelet embryo features by multi-resolution 2D wavelet discrete transform, followed by min-redundancy max-relevance feature selection, which yields optimal distinguishing features for an annotation. We then construct a series of parallel bi-class predictors to solve the multi-objective annotation problem since each image may correspond to multiple annotations. Supplementary information: The complete annotation prediction results are available at: http://www.cs.niu.edu/~jzhou/papers/fruitfly and http://research.janelia.org/peng/proj/fly_embryo_annotation/. The datasets used in experiments will be available upon request to the correspondence author. Contact:jzhou@cs.niu.edu and pengh@janelia.hhmi.org