DocumentCode :
2192747
Title :
Curve Profiling Feature: Novel Compact Representation for Drosophila Embryonic Gene Expression Pattern Mining
Author :
Fang, Chunsheng ; Zhang, Minlu ; Ralescu, Anca L. ; Lu, Jason L.
Author_Institution :
Div. of Biomed. Inf., Cincinnati Children´´s Hosp. Res. Found., Cincinnati, OH, USA
fYear :
2010
fDate :
13-13 Dec. 2010
Firstpage :
695
Lastpage :
702
Abstract :
Curve Profiling Feature (CPF) is an innovative compact and discriminative feature for representing and mining the temporal-spatial patterns underlying Drosophila embryonic gene expressions from the Berkeley Drosophila Genome Project (BDGP) in situ hybridization (ISH) database. CPF is calibration-free, unaffected by differences in individual embryonic size or shape, biologically inspired, and can significantly reduce data dimensionality. Moreover, CPF can identify spatial periodic patterns - a nontrivial concern by previous methods. Quantitative evaluations by controlled vocabulary annotation prediction and gene function enrichment with Gene Ontology knowledge base showed that our CPF achieves comparable performance as state-of-the-art Bag-Of-Words model while requires much less space and time. Application systems are also proposed to help biologists in different aspects including predicting annotations and gene functional enrichment, visualization based on manifold learning, content-based gene expression pattern retrieval with synthesized query.
Keywords :
biology computing; data mining; ontologies (artificial intelligence); Berkeley Drosophila Genome Project; Drosophila; curve profiling feature; embryonic gene expression pattern mining; gene ontology functional enrichment; in situ hybridization database; Drosophila embryonic gene expression; annotation prediction; feature representation; gene ontology functional enrichment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9244-2
Electronic_ISBN :
978-0-7695-4257-7
Type :
conf
DOI :
10.1109/ICDMW.2010.67
Filename :
5693364
Link To Document :
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