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Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data

Overview of attention for article published in BMC Medical Genomics, December 2016
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1 tweeter

Citations

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6 Dimensions

Readers on

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12 Mendeley
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Title
Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data
Published in
BMC Medical Genomics, December 2016
DOI 10.1186/s12920-016-0233-2
Pubmed ID
Authors

Harsh Saini, Sunil Pranit Lal, Vimal Vikash Naidu, Vincel Wince Pickering, Gurmeet Singh, Tatsuhiko Tsunoda, Alok Sharma

Abstract

High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 50%
Professor 3 25%
Student > Bachelor 1 8%
Professor > Associate Professor 1 8%
Unknown 1 8%
Readers by discipline Count As %
Computer Science 3 25%
Business, Management and Accounting 2 17%
Agricultural and Biological Sciences 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Medicine and Dentistry 1 8%
Other 1 8%
Unknown 3 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 October 2017.
All research outputs
#10,561,860
of 11,918,812 outputs
Outputs from BMC Medical Genomics
#492
of 549 outputs
Outputs of similar age
#267,053
of 326,717 outputs
Outputs of similar age from BMC Medical Genomics
#10
of 11 outputs
Altmetric has tracked 11,918,812 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 549 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 1st percentile – i.e., 1% 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 326,717 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.