Drosophila gene expression pattern annotation through multi-instance multi-label learning.
IF: 3.702
Cited by: 39


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.


Temporal Spatial Anatomic
Temporal Spatial Gene Expression

MeSH terms

Computational Biology
Databases, Factual
Embryo, Nonmammalian
Gene Expression Regulation, Developmental
Molecular Sequence Annotation
Support Vector Machine


Li, Ying-Xin
Ji, Shuiwang
Kumar, Sudhir
Ye, Jieping
Zhou, Zhi-Hua

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