Issue No. 01 - January/February (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.73
Ying-Xin Li , Nat. Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China
Shuiwang Ji , Dept. of Comput. Sci., Old Dominion Univ., Norfolk, VA, USA
S. Kumar , Center for Evolutionary Med. & Inf., Arizona State Univ., Tempe, AZ, USA
Jieping Ye , Center for Evolutionary Med. & Inf., Arizona State Univ., Tempe, AZ, USA
Zhi-Hua Zhou , Nat. Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China
In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time. Gene expressions captured in these images have been manually annotated with anatomical and developmental ontology terms using a controlled vocabulary (CV), which are useful in research aimed at understanding gene functions, interactions, and networks. With the rapid accumulation of images, the process of manual annotation has become increasingly cumbersome, and computational methods to automate this task are urgently needed. However, the automated annotation of embryo images is challenging. This is because the annotation terms spatially correspond to local expression patterns of images, yet they are assigned collectively to groups of images and it is unknown which term corresponds to which region of which image in the group. In this paper, we address this problem using a new machine learning framework, Multi-Instance Multi-Label (MIML) learning. We first show that the underlying nature of the annotation task is a typical MIML learning problem. Then, we propose two support vector machine algorithms under the MIML framework for the task. Experimental results on the FlyExpress database (a digital library of standardized Drosophila gene expression pattern images) reveal that the exploitation of MIML framework leads to significant performance improvement over state-of-the-art approaches.
Gene expression, Databases, Embryo, Machine learning, Bioinformatics, Head, Computational biology
Ying-Xin Li, Shuiwang Ji, S. Kumar, Jieping Ye and Zhi-Hua Zhou, "Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 1, pp. 98-112, 2011.