TY - JOUR T1 - Automatically Identifying and Annotating Mouse Embryo Gene Expression Patterns JF - Bioinformatics Y1 - 2011 A1 - Liangxiu Han A1 - van Hemert, Jano A1 - Richard Baldock KW - classification KW - e-Science AB - Motivation: Deciphering the regulatory and developmental mechanisms for multicellular organisms requires detailed knowledge of gene interactions and gene expressions. The availability of large datasets with both spatial and ontological annotation of the spatio-temporal patterns of gene-expression in mouse embryo provides a powerful resource to discover the biological function of embryo organisation. Ontological annotation of gene expressions consists of labelling images with terms from the anatomy ontology for mouse development. If the spatial genes of an anatomical component are expressed in an image, the image is then tagged with a term of that anatomical component. The current annotation is done manually by domain experts, which is both time consuming and costly. In addition, the level of detail is variable and inevitably, errors arise from the tedious nature of the task. In this paper, we present a new method to automatically identify and annotate gene expression patterns in the mouse embryo with anatomical terms. Results: The method takes images from in situ hybridisation studies and the ontology for the developing mouse embryo, it then combines machine learning and image processing techniques to produce classifiers that automatically identify and annotate gene expression patterns in these images.We evaluate our method on image data from the EURExpress-II study where we use it to automatically classify nine anatomical terms: humerus, handplate, fibula, tibia, femur, ribs, petrous part, scapula and head mesenchyme. The accuracy of our method lies between 70–80% with few exceptions. Conclusions: We show that other known methods have lower classification performance than ours. We have investigated the images misclassified by our method and found several cases where the original annotation was not correct. This shows our method is robust against this kind of noise. Availability: The annotation result and the experimental dataset in the paper can be freely accessed at http://www2.docm.mmu.ac.uk/STAFF/L.Han/geneannotation/ Contact: l.han@mmu.ac.uk, j.vanhemert@ed.ac.uk and Richard.Baldock@hgu.mrc.ac.uk VL - 27 UR - http://bioinformatics.oxfordjournals.org/content/early/2011/02/25/bioinformatics.btr105.abstract ER - TY - CONF T1 - Automating Gene Expression Annotation for Mouse Embryo T2 - Lecture Notes in Computer Science (Advanced Data Mining and Applications, 5th International Conference) Y1 - 2009 A1 - Liangxiu Han A1 - van Hemert, Jano A1 - Richard Baldock A1 - Atkinson, Malcolm P. ED - Ronghuai Huang ED - Qiang Yang ED - Jian Pei ED - et al JF - Lecture Notes in Computer Science (Advanced Data Mining and Applications, 5th International Conference) PB - Springer VL - LNAI 5678 ER - TY - CHAP T1 - COBrA and COBrA-CT: Ontology Engineering Tools T2 - Anatomy Ontologies for Bioinformatics: Principles and Practice Y1 - 2007 A1 - Stuart Aitken A1 - Yin Chen ED - Albert Burger ED - Duncan Davidson ED - Richard Baldock AB - COBrA is a Java-based ontology editor for bio-ontologies and anatomies that dif- fers from other editors by supporting the linking of concepts between two ontologies, and providing sophisticated analysis and verification functions. In addition to the Gene Ontology and Open Biology Ontologies formats, COBrA can import and export ontologies in the Se- mantic Web formats RDF, RDFS and OWL. COBrA is being re-engineered as a Prot ́eg ́e plug-in, and complemented by an ontology server and a tool for the management of ontology versions and collaborative ontology de- velopment. We describe both the original COBrA tool and the current developments in this chapter. JF - Anatomy Ontologies for Bioinformatics: Principles and Practice PB - Springer SN - ISBN-10:1846288843 UR - http://www.amazon.ca/Anatomy-Ontologies-Bioinformatics-Principles-Practice/dp/1846288843 ER -