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CUDA-Accelerated Data-Mining for Putative Heteromeric Transcription Factors and Target Genes Using Microarray Gene Expression Profiles

Edward A. Salinas, Amitava Karmaker
Independent Researcher, Rockville, Maryland 20852, USA
The 2012 International Conference on Bioinformatics and Computational Biology (BIOCOMP’12), 2012

@article{salinas2012cuda,

   title={CUDA-Accelerated Data-Mining for Putative Heteromeric Transcription Factors and Target Genes Using Microarray Gene Expression Profiles},

   author={Salinas, E.A. and Karmaker, A.},

   year={2012}

}

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Understanding protein-protein and protein-DNA interactions is key to understanding the dynamics of gene regulation [3,17]. We here review a previously presented method[1,15,20], based on a variation of microarray expression profile correlation analysis, that seeks to identify interactions between a putative heteropolymeric transcription factor(TF) complex and DNA as well as some experimental results that bolster the argument for the method’s validity. The method incorporates correlation coefficients between genes and transcription factors expression profiles, but also between genes and hypothetical TF co-factors, whose expression profiles are estimated by taking minima from constituent profiles. Second, we extend the technique to search for fourth-order protein interactions (k=4). Since a CPU-based analysis would require an execution time on the order of months, we have implemented the k=4 analysis on a CUDA-enabled NVIDIA GPU [16]. With CUDA, we achieved speedups of about 6-fold. Finally, we present the results of the higher order analysis and discuss those results as well as the implementation of the method using CUDA. To our knowledge CUDA has never been used to implement this particular algorithm for microarray gene expression profile analysis.
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