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The growth of public DNA sequence and metagenomic data over the last two decades has been exponential. Bioinformatics researchers are confronted with analysis of massive data sets, while the problem is still growing at an alarming rate in the near future. In this paper, we integrate many open source software tools in biological sequences analysis to construct an effective cloud-based microbial metagenomics...
In this paper, we propose algorithms for biomolecular docking sites selection problem by various machine learning approaches with selective features reduction. The proposed method can reduce the number of various amino acid features before constructing machine learning prediction models. Given frame boxes with features, the proposed method analyzes the important features by correlation coefficients...
With the rapid growth of biological technology, large amount of biological data can be produced in few days or months. Many of common-used tools become computation-consuming in analyzing such big biological data. Cloud computing has emerged to provide the huge amount of computing power and play important role in development of bioinformatics tools. We propose a cloud computing framework that is able...
Currently, cloud computing has been applied to share computing resources to achieve coherence and economies of scale similar to a utility over a network. Hadoop is an widely-used open-source cloud computing environment that implements the Google MapReduce framework. Many bioinformatics tools have been developed to provide cloud services by using Hadoop. This paper proposes approaches in providing...
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