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G.A.M.E.: GPU-accelerated mixture elucidator

Overview of attention for article published in Journal of Cheminformatics, September 2017
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G.A.M.E.: GPU-accelerated mixture elucidator
Published in
Journal of Cheminformatics, September 2017
DOI 10.1186/s13321-017-0238-7
Pubmed ID

Alioune Schurz, Bo-Han Su, Yi-Shu Tu, Tony Tsung-Yu Lu, Olivia A. Lin, Yufeng J. Tseng


GPU acceleration is useful in solving complex chemical information problems. Identifying unknown structures from the mass spectra of natural product mixtures has been a desirable yet unresolved issue in metabolomics. However, this elucidation process has been hampered by complex experimental data and the inability of instruments to completely separate different compounds. Fortunately, with current high-resolution mass spectrometry, one feasible strategy is to define this problem as extending a scaffold database with sidechains of different probabilities to match the high-resolution mass obtained from a high-resolution mass spectrum. By introducing a dynamic programming (DP) algorithm, it is possible to solve this NP-complete problem in pseudo-polynomial time. However, the running time of the DP algorithm grows by orders of magnitude as the number of mass decimal digits increases, thus limiting the boost in structural prediction capabilities. By harnessing the heavily parallel architecture of modern GPUs, we designed a "compute unified device architecture" (CUDA)-based GPU-accelerated mixture elucidator (G.A.M.E.) that considerably improves the performance of the DP, allowing up to five decimal digits for input mass data. As exemplified by four testing datasets with verified constitutions from natural products, G.A.M.E. allows for efficient and automatic structural elucidation of unknown mixtures for practical procedures. Graphical abstract .

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Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 67%
Student > Bachelor 1 17%
Researcher 1 17%
Readers by discipline Count As %
Chemistry 3 50%
Agricultural and Biological Sciences 2 33%
Biochemistry, Genetics and Molecular Biology 1 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 September 2017.
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Altmetric has tracked 11,799,674 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 462 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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