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A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

Overview of attention for article published in BMC Genomics, January 2015
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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Title
A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data
Published in
BMC Genomics, January 2015
DOI 10.1186/1471-2164-16-s1-s2
Pubmed ID
Authors

Anna L Swan, Dov J Stekel, Charlie Hodgman, David Allaway, Mohammed H Alqahtani, Ali Mobasheri, Jaume Bacardit

Abstract

Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets. Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large 'omics' datasets are increasingly being used in the area of rheumatology. Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
France 1 <1%
Singapore 1 <1%
Unknown 130 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 20%
Student > Ph. D. Student 26 19%
Student > Master 15 11%
Professor 9 7%
Student > Bachelor 9 7%
Other 21 16%
Unknown 27 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 15%
Computer Science 20 15%
Medicine and Dentistry 15 11%
Biochemistry, Genetics and Molecular Biology 14 10%
Engineering 13 10%
Other 14 10%
Unknown 38 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 29 January 2016.
All research outputs
#12,716,412
of 22,778,347 outputs
Outputs from BMC Genomics
#4,394
of 10,643 outputs
Outputs of similar age
#173,360
of 379,767 outputs
Outputs of similar age from BMC Genomics
#108
of 280 outputs
Altmetric has tracked 22,778,347 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,643 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 57% 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 379,767 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 280 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.