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.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Capsule Network Based Modeling of Multi-omics Data for Discovery of Breast Cancer-Related Genes
|
|---|---|
| Published in |
IEEE/ACM Transactions on Computational Biology and Bioinformatics, April 2019
|
| DOI | 10.1109/tcbb.2019.2909905 |
| Pubmed ID | |
| Authors |
Chen Peng, Yang Zheng, De-Shuang Huang |
| Abstract |
Breast cancer is one of the most common cancers all over the world, which bring about more than 450,000 deaths each year. Although this malignancy has been extensively studied by a large number of researchers, its prognosis is still poor. Since therapeutic advance can be obtained based on gene signatures, there is an urgent need to discover genes related to breast cancer that may help uncover the mechanisms in cancer progression. We propose a deep learning method for the discovery of breast cancer-related genes by using Capsule Network based Modeling of Multi-omics Data (CapsNetMMD). In CapsNetMMD, we make use of known breast cancer-related genes to transform the issue of gene identification into the issue of supervised classification. The features of genes are generated through comprehensive in-tegration of multi-omics data, e.g., mRNA expression, z scores for mRNA expression, DNA methylation and two forms of DNA copy-number alterations (CNAs). By modeling features based on cap-sule network, we identify breast cancer-related genes with a significantly better performance than other existing machine learning methods. The predicted genes with prognostic values play potential important roles in breast cancer and may serve as candidates for biologists and medical scientists in the future studies of biomarkers. |
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 % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 84 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 14% |
| Researcher | 10 | 12% |
| Student > Master | 7 | 8% |
| Student > Doctoral Student | 5 | 6% |
| Student > Bachelor | 5 | 6% |
| Other | 16 | 19% |
| Unknown | 29 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 23 | 27% |
| Biochemistry, Genetics and Molecular Biology | 7 | 8% |
| Medicine and Dentistry | 4 | 5% |
| Agricultural and Biological Sciences | 3 | 4% |
| Immunology and Microbiology | 2 | 2% |
| Other | 10 | 12% |
| Unknown | 35 | 42% |
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 06 May 2019.
All research outputs
#20,667,544
of 25,385,509 outputs
Outputs from IEEE/ACM Transactions on Computational Biology and Bioinformatics
#671
of 1,081 outputs
Outputs of similar age
#281,802
of 366,320 outputs
Outputs of similar age from IEEE/ACM Transactions on Computational Biology and Bioinformatics
#9
of 13 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,081 research outputs from this source. They receive a mean Attention Score of 2.4. This one is in the 24th percentile – i.e., 24% 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 366,320 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.