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Probabilistic Topic Modeling using LDA of Taxonomic Structure of Genomic Data?

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.3, No. 5)

Publication Date:

Authors : ; ;

Page : 257-262

Keywords : Data mining; Bioinformatics (genome or protein) databases; Language models; Metagenomics;

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Many data mining techniques have been proposed for fulfilling various knowledge discover tasks in order to achieve the goal of retrieving useful information for user. Various type of probabilistic topic modeling use LDA model of taxonomic structure of genomic data. It can be generated using the techniques, such as that enable homology based approach for further study the functional core. In the planned method, the recognition of key functionality groups is achieved by generative topic modeling. This paper present the research on concept of developing an effective taxonomy model of data analysis, How effectively to extract useful information from unlabeled data. Most of generative the topic model can be used to the model of the large of data 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 large quantity data will uncover the distribution over latent functions (latent topic) in each sample.

Last modified: 2014-05-16 20:40:56