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Mendeley readers
Attention Score in Context
| Title |
Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory
|
|---|---|
| Published in |
BMC Bioinformatics, January 2016
|
| DOI | 10.1186/s12859-015-0845-0 |
| Pubmed ID | |
| Authors |
Chien-Hung Huang, Peter Mu-Hsin Chang, Chia-Wei Hsu, Chi-Ying F. Huang, Ka-Lok Ng |
| Abstract |
Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects. This work integrates two approaches - machine learning algorithms and topological parameter-based classification - to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments. A total of 7 (8) and 11 (12) promising drugs (targeted genes) were discovered for treating early- and late-stage NSCLC, respectively. The effectiveness of these drugs is supported by the literature, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC50 measurements. With the novel pipeline of drug repositioning, the discovery of enriched pathways and potential drugs related to NSCLC can provide insight into the key regulators of tumorigenesis and the treatment of NSCLC. Based on the verified effectiveness of the targeted drugs predicted by this pipeline, we suggest that our drug-finding pipeline is effective for repositioning drugs. |
Mendeley readers
The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 90 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 14% |
| Student > Master | 10 | 11% |
| Student > Bachelor | 9 | 10% |
| Researcher | 8 | 9% |
| Student > Doctoral Student | 6 | 7% |
| Other | 19 | 21% |
| Unknown | 25 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 12 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 9 | 10% |
| Biochemistry, Genetics and Molecular Biology | 9 | 10% |
| Computer Science | 9 | 10% |
| Engineering | 6 | 7% |
| Other | 16 | 18% |
| Unknown | 29 | 32% |
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 16 January 2016.
All research outputs
#20,302,535
of 22,840,638 outputs
Outputs from BMC Bioinformatics
#6,861
of 7,288 outputs
Outputs of similar age
#331,662
of 394,936 outputs
Outputs of similar age from BMC Bioinformatics
#134
of 143 outputs
Altmetric has tracked 22,840,638 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 7,288 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 394,936 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 143 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.