TY - GEN
T1 - Drosophila gene expression pattern annotation through multi-instance multi-label learning
AU - Li, Ying Xin
AU - Ji, Shuiwang
AU - Kumar, Sudhir
AU - Ye, Jieping
AU - Zhou, Zhi Hua
PY - 2009/1/1
Y1 - 2009/1/1
N2 - The Berkeley Drosophila Genome Project (BDGP) has produced a large number of gene expression patterns, many of which have been annotated textually with anatomical and developmental terms. These terms spatially correspond to local regions of the images; however, they are attached collectively to groups of images, such that it is unknown which term is assigned to which region of which image in the group. This poses a challenge to the development of the computational method to automate the textual description of expression patterns contained in each image. In this paper, we show that the underlying nature of this task matches well with a new machine learning framework,Multi-Instance Multi-Label learning (MIML). We propose a new MIML support vector machine to solve the problems that beset the annotation task. Empirical study shows that the proposed method outperforms the state-of-the-art Drosophila gene expression pattern annotation methods.
AB - The Berkeley Drosophila Genome Project (BDGP) has produced a large number of gene expression patterns, many of which have been annotated textually with anatomical and developmental terms. These terms spatially correspond to local regions of the images; however, they are attached collectively to groups of images, such that it is unknown which term is assigned to which region of which image in the group. This poses a challenge to the development of the computational method to automate the textual description of expression patterns contained in each image. In this paper, we show that the underlying nature of this task matches well with a new machine learning framework,Multi-Instance Multi-Label learning (MIML). We propose a new MIML support vector machine to solve the problems that beset the annotation task. Empirical study shows that the proposed method outperforms the state-of-the-art Drosophila gene expression pattern annotation methods.
UR - http://www.scopus.com/inward/record.url?scp=77953193733&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953193733&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77953193733
SN - 9781577354260
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 1445
EP - 1450
BT - IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence
PB - International Joint Conferences on Artificial Intelligence
T2 - 21st International Joint Conference on Artificial Intelligence, IJCAI 2009
Y2 - 11 July 2009 through 16 July 2009
ER -