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Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning

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Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning

Content Provider	ACM Digital Library
Author	Kumar, Sudhir Ye, Jieping Ji, Shuiwang Li, Ying-Xin Zhou, Zhi-Hua
Abstract	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.
Starting Page	98
Ending Page	112
Page Count	15
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2011.73
Volume Number	9
Issue Number	1
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-01-01
Access Restriction	Subscribed
Subject Keyword	Gene expression pattern, image annotation, machine learning, multi-instance multi-label (MIML) learning, support vector machine, Drosophila.
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning

Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning

Drosophila Gene Expression Pattern Annotation Through Multi-Instance MultiLabel (2009)

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

Drosophila Gene Expression Pattern Annotation Through Multi-Instance MultiLabel (2009)

Rank-loss support instance machines for MIML instance annotation

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

Instance Annotation for Multi-Instance Multi-Label Learning

Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning*

Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning

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