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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data
|
|---|---|
| Published in |
BMC Bioinformatics, February 2015
|
| DOI | 10.1186/s12859-015-0463-x |
| Pubmed ID | |
| Authors |
Hui-Ling Huang, Yu-Chung Wu, Li-Jen Su, Yun-Ju Huang, Phasit Charoenkwan, Wen-Liang Chen, Hua-Chin Lee, William Cheng-Chung Chu, Shinn-Ying Ho |
| Abstract |
Few studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial-mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients' survival. This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies. The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 31 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 25% |
| Researcher | 5 | 16% |
| Student > Master | 4 | 13% |
| Student > Bachelor | 3 | 9% |
| Other | 3 | 9% |
| Other | 4 | 13% |
| Unknown | 5 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 25% |
| Biochemistry, Genetics and Molecular Biology | 5 | 16% |
| Agricultural and Biological Sciences | 5 | 16% |
| Medicine and Dentistry | 2 | 6% |
| Engineering | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 6 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 01 April 2015.
All research outputs
#14,217,957
of 22,792,160 outputs
Outputs from BMC Bioinformatics
#4,720
of 7,280 outputs
Outputs of similar age
#133,590
of 255,034 outputs
Outputs of similar age from BMC Bioinformatics
#80
of 137 outputs
Altmetric has tracked 22,792,160 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,280 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 31st percentile – i.e., 31% 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 255,034 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.