Drosophila gene expression pattern annotation through multi-instance multi-label learning

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

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


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.

Original languageEnglish (US)
Article number5753882
Pages (from-to)98-112
Number of pages15
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number1
StatePublished - 2012


  • Drosophila
  • Gene expression pattern
  • image annotation
  • machine learning
  • multi-instance multi-label (MIML) learning
  • support vector machine

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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