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Interpolation based consensus clustering for gene expression time series

Overview of attention for article published in BMC Bioinformatics, April 2015
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Title
Interpolation based consensus clustering for gene expression time series
Published in
BMC Bioinformatics, April 2015
DOI 10.1186/s12859-015-0541-0
Pubmed ID
Authors

Tai-Yu Chiu, Ting-Chieh Hsu, Chia-Cheng Yen, Jia-Shung Wang

Abstract

Unsupervised analyses such as clustering are the essential tools required to interpret time-series expression data from microarrays. Several clustering algorithms have been developed to analyze gene expression data. Early methods such as k-means, hierarchical clustering, and self-organizing maps are popular for their simplicity. However, because of noise and uncertainty of measurement, these common algorithms have low accuracy. Moreover, because gene expression is a temporal process, the relationship between successive time points should be considered in the analyses. In addition, biological processes are generally continuous; therefore, the datasets collected from time series experiments are often found to have an insufficient number of data points and, as a result, compensation for missing data can also be an issue. An affinity propagation-based clustering algorithm for time-series gene expression data is proposed. The algorithm explores the relationship between genes using a sliding-window mechanism to extract a large number of features. In addition, the time-course datasets are resampled with spline interpolation to predict the unobserved values. Finally, a consensus process is applied to enhance the robustness of the method. Some real gene expression datasets were analyzed to demonstrate the accuracy and efficiency of the algorithm. The proposed algorithm has benefitted from the use of cubic B-splines interpolation, sliding-window, affinity propagation, gene relativity graph, and a consensus process, and, as a result, provides both appropriate and effective clustering of time-series gene expression data. The proposed method was tested with gene expression data from the Yeast galactose dataset, the Yeast cell-cycle dataset (Y5), and the Yeast sporulation dataset, and the results illustrated the relationships between the expressed genes, which may give some insights into the biological processes involved.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 6%
Canada 1 6%
Brazil 1 6%
Unknown 14 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 29%
Student > Master 3 18%
Professor > Associate Professor 3 18%
Student > Doctoral Student 3 18%
Student > Ph. D. Student 2 12%
Other 0 0%
Unknown 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 41%
Mathematics 3 18%
Computer Science 2 12%
Biochemistry, Genetics and Molecular Biology 1 6%
Decision Sciences 1 6%
Other 2 12%
Unknown 1 6%

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 30 May 2015.
All research outputs
#6,394,479
of 11,233,326 outputs
Outputs from BMC Bioinformatics
#2,577
of 4,193 outputs
Outputs of similar age
#99,916
of 211,855 outputs
Outputs of similar age from BMC Bioinformatics
#92
of 132 outputs
Altmetric has tracked 11,233,326 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,193 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 211,855 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.