↓ Skip to main content

Multi-TGDR, a multi-class regularization method, identifies the metabolic profiles of hepatocellular carcinoma and cirrhosis infected with hepatitis B or hepatitis C virus

Overview of attention for article published in BMC Bioinformatics, April 2014
Altmetric Badge

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
26 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Multi-TGDR, a multi-class regularization method, identifies the metabolic profiles of hepatocellular carcinoma and cirrhosis infected with hepatitis B or hepatitis C virus
Published in
BMC Bioinformatics, April 2014
DOI 10.1186/1471-2105-15-97
Pubmed ID
Authors

Suyan Tian, Howard H Chang, Chi Wang, Jing Jiang, Xiaomei Wang, Junqi Niu

Abstract

Over the last decade, metabolomics has evolved into a mainstream enterprise utilized by many laboratories globally. Like other "omics" data, metabolomics data has the characteristics of a smaller sample size compared to the number of features evaluated. Thus the selection of an optimal subset of features with a supervised classifier is imperative. We extended an existing feature selection algorithm, threshold gradient descent regularization (TGDR), to handle multi-class classification of "omics" data, and proposed two such extensions referred to as multi-TGDR. Both multi-TGDR frameworks were used to analyze a metabolomics dataset that compares the metabolic profiles of hepatocellular carcinoma (HCC) infected with hepatitis B (HBV) or C virus (HCV) with that of cirrhosis induced by HBV/HCV infection; the goal was to improve early-stage diagnosis of HCC.

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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 8%
Russia 1 4%
Unknown 23 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 38%
Student > Postgraduate 3 12%
Student > Master 3 12%
Student > Doctoral Student 2 8%
Other 2 8%
Other 5 19%
Unknown 1 4%
Readers by discipline Count As %
Medicine and Dentistry 8 31%
Agricultural and Biological Sciences 6 23%
Biochemistry, Genetics and Molecular Biology 2 8%
Computer Science 2 8%
Mathematics 1 4%
Other 3 12%
Unknown 4 15%

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 04 April 2014.
All research outputs
#18,369,403
of 22,751,628 outputs
Outputs from BMC Bioinformatics
#6,301
of 7,269 outputs
Outputs of similar age
#163,661
of 226,135 outputs
Outputs of similar age from BMC Bioinformatics
#93
of 114 outputs
Altmetric has tracked 22,751,628 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,269 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 226,135 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 114 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.