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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer
|
|---|---|
| Published in |
BMC Bioinformatics, May 2015
|
| DOI | 10.1186/s12859-015-0565-5 |
| Pubmed ID | |
| Authors |
Vasily Sachnev, Saras Saraswathi, Rashid Niaz, Andrzej Kloczkowski, Sundaram Suresh |
| Abstract |
Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific cancers. Identification of molecular markers that are related to different types of cancers might lead to discovery of drugs that are patient and disease specific. This study aims to use microarray gene expression cancer data to identify biomarkers that are indicative of different types of cancers. Our aim is to provide a multi-class cancer classifier that can simultaneously differentiate between cancers and identify type-specific biomarkers, through the application of the Binary Coded Genetic Algorithm (BCGA) and a neural network based Extreme Learning Machine (ELM) algorithm. BCGA and ELM are combined and used to select a subset of genes that are present in the Global Cancer Mapping (GCM) data set. This set of candidate genes contains over 52 biomarkers that are related to multiple cancers, according to the literature. They include APOA1, VEGFC, YWHAZ, B2M, EIF2S1, CCR9 and many other genes that have been associated with the hallmarks of cancer. BCGA-ELM is tested on several cancer data sets and the results are compared to other classification methods. BCGA-ELM compares or exceeds other algorithms in terms of accuracy. We were also able to show that over 50 % of genes selected by BCGA-ELM on GCM data are cancer related biomarkers. We were able to simultaneously differentiate between 14 different types of cancers, using only 92 genes, to achieve a multi-class classification accuracy of 95.4 % which is between 21.6 % and 38 % higher than other results in the literature for multi-class cancer classification. Our findings suggest that computational algorithms such as BCGA-ELM can facilitate biomarker-driven integrated cancer research that can lead to a detailed understanding of the complexities of cancer. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 2% |
| Unknown | 40 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 22% |
| Student > Bachelor | 5 | 12% |
| Researcher | 4 | 10% |
| Student > Master | 4 | 10% |
| Student > Doctoral Student | 3 | 7% |
| Other | 8 | 20% |
| Unknown | 8 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 17% |
| Medicine and Dentistry | 6 | 15% |
| Biochemistry, Genetics and Molecular Biology | 6 | 15% |
| Agricultural and Biological Sciences | 4 | 10% |
| Immunology and Microbiology | 2 | 5% |
| Other | 5 | 12% |
| Unknown | 11 | 27% |
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 21 May 2015.
All research outputs
#20,273,512
of 22,805,349 outputs
Outputs from BMC Bioinformatics
#6,853
of 7,281 outputs
Outputs of similar age
#223,232
of 266,611 outputs
Outputs of similar age from BMC Bioinformatics
#112
of 121 outputs
Altmetric has tracked 22,805,349 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 7,281 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 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 266,611 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 121 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.