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Minimum redundancy maximum relevance feature selection approach for temporal gene expression data

Overview of attention for article published in BMC Bioinformatics, January 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

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1 news outlet
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2 tweeters

Citations

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208 Dimensions

Readers on

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257 Mendeley
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Title
Minimum redundancy maximum relevance feature selection approach for temporal gene expression data
Published in
BMC Bioinformatics, January 2017
DOI 10.1186/s12859-016-1423-9
Pubmed ID
Authors

Milos Radovic, Mohamed Ghalwash, Nenad Filipovic, Zoran Obradovic

Abstract

Feature selection, aiming to identify a subset of features among a possibly large set of features that are relevant for predicting a response, is an important preprocessing step in machine learning. In gene expression studies this is not a trivial task for several reasons, including potential temporal character of data. However, most feature selection approaches developed for microarray data cannot handle multivariate temporal data without previous data flattening, which results in loss of temporal information. We propose a temporal minimum redundancy - maximum relevance (TMRMR) feature selection approach, which is able to handle multivariate temporal data without previous data flattening. In the proposed approach we compute relevance of a gene by averaging F-statistic values calculated across individual time steps, and we compute redundancy between genes by using a dynamical time warping approach. The proposed method is evaluated on three temporal gene expression datasets from human viral challenge studies. Obtained results show that the proposed method outperforms alternatives widely used in gene expression studies. In particular, the proposed method achieved improvement in accuracy in 34 out of 54 experiments, while the other methods outperformed it in no more than 4 experiments. We developed a filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria. The proposed method incorporates temporal information by combining relevance, which is calculated as an average F-statistic value across different time steps, with redundancy, which is calculated by employing dynamical time warping approach. As evident in our experiments, incorporating the temporal information into the feature selection process leads to selection of more discriminative features.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 257 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 <1%
Unknown 256 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 18%
Student > Master 36 14%
Researcher 32 12%
Student > Bachelor 27 11%
Lecturer 13 5%
Other 45 18%
Unknown 57 22%
Readers by discipline Count As %
Computer Science 63 25%
Engineering 47 18%
Biochemistry, Genetics and Molecular Biology 18 7%
Medicine and Dentistry 16 6%
Agricultural and Biological Sciences 11 4%
Other 32 12%
Unknown 70 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 07 May 2022.
All research outputs
#2,849,509
of 23,003,906 outputs
Outputs from BMC Bioinformatics
#969
of 7,312 outputs
Outputs of similar age
#59,801
of 421,740 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 138 outputs
Altmetric has tracked 23,003,906 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,312 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 86% of its peers.
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 421,740 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.