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Exploiting the Functional and Taxonomic Structure of Genomic Data by Probabilistic Topic Modeling
July-Aug. 2012 (vol. 9 no. 4)
pp. 980-991
ASCII Text
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T. Y. Lim, Xiaohua Hu, Xin Chen, Xiajiong Shen, E. K. Park, G. L. Rosen, "Exploiting the Functional and Taxonomic Structure of Genomic Data by Probabilistic Topic Modeling," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 4, pp. 980-991, July-Aug., 2012.
 
 
BibTex
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@article{ 10.1109/TCBB.2011.113,
author = {T. Y. Lim and Xiaohua Hu and Xin Chen and Xiajiong Shen and E. K. Park and G. L. Rosen},
title = {Exploiting the Functional and Taxonomic Structure of Genomic Data by Probabilistic Topic Modeling},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {9},
number = {4},
issn = {1545-5963},
year = {2012},
pages = {980-991},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.113},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Exploiting the Functional and Taxonomic Structure of Genomic Data by Probabilistic Topic Modeling
IS - 4
SN - 1545-5963
SP980
EP991
EPD - 980-991
A1 - T. Y. Lim,
A1 - Xiaohua Hu,
A1 - Xin Chen,
A1 - Xiajiong Shen,
A1 - E. K. Park,
A1 - G. L. Rosen,
PY - 2012
KW - probability
KW - bioinformatics
KW - data mining
KW - DNA
KW - genetics
KW - genomics
KW - molecular biophysics
KW - data mining
KW - functional structure
KW - taxonomic structure
KW - genomic data
KW - probabilistic topic modeling
KW - homology-based approach
KW - composition-based approach
KW - microbial core
KW - gene core
KW - generative topic modeling
KW - taxon abundance information
KW - genome-level composition
KW - DNA subreads
KW - latten genetic patterns
KW - bioinformatics
KW - Bioinformatics
KW - Genomics
KW - Strain
KW - Databases
KW - Data models
KW - DNA
KW - Integrated circuit modeling
KW - metagenomics.
KW - Data mining
KW - bioinformatics (genome or protein) databases
KW - language models
VL - 9
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
T. Y. Lim, Coll. of Comput. & Inf. Eng., Henan Univ., Kaifeng, China
Xiaohua Hu, Coll. of Inf. Sci. & Technol., Drexel Univ., Philadelphia, PA, USA
Xin Chen, Coll. of Inf. Sci. & Technol., Drexel Univ., Philadelphia, PA, USA
Xiajiong Shen, California State Univ. - Chico, Chico, CA, USA
E. K. Park, Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA
G. L. Rosen, Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA
In this paper, we present a method that enable both homology-based approach and composition-based approach to further study the functional core (i.e., microbial core and gene core, correspondingly). In the proposed method, the identification of major functionality groups is achieved by generative topic modeling, which is able to extract useful information from unlabeled data. We first show that generative topic model can be used to model the taxon abundance information obtained by homology-based approach and study the microbial core. The model considers each sample as a "document,” which has a mixture of functional groups, while each functional group (also known as a "latent topic”) is a weight mixture of species. Therefore, estimating the generative topic model for taxon abundance data will uncover the distribution over latent functions (latent topic) in each sample. Second, we show that, generative topic model can also be used to study the genome-level composition of "N-mer” features (DNA subreads obtained by composition-based approaches). The model consider each genome as a mixture of latten genetic patterns (latent topics), while each functional pattern is a weighted mixture of the "N-mer” features, thus the existence of core genomes can be indicated by a set of common N-mer features. After studying the mutual information between latent topics and gene regions, we provide an explanation of the functional roles of uncovered latten genetic patterns. The experimental results demonstrate the effectiveness of proposed method.

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Index Terms:
probability,bioinformatics,data mining,DNA,genetics,genomics,molecular biophysics,data mining,functional structure,taxonomic structure,genomic data,probabilistic topic modeling,homology-based approach,composition-based approach,microbial core,gene core,generative topic modeling,taxon abundance information,genome-level composition,DNA subreads,latten genetic patterns,bioinformatics,Bioinformatics,Genomics,Strain,Databases,Data models,DNA,Integrated circuit modeling,metagenomics.,Data mining,bioinformatics (genome or protein) databases,language models
Citation:
T. Y. Lim, Xiaohua Hu, Xin Chen, Xiajiong Shen, E. K. Park, G. L. Rosen, "Exploiting the Functional and Taxonomic Structure of Genomic Data by Probabilistic Topic Modeling," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 4, pp. 980-991, July-Aug. 2012, doi:10.1109/TCBB.2011.113
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