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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Deep Learning on Chromatin Accessibility
|
|---|---|
| Chapter number | 18 |
| Book title |
Chromatin Accessibility
|
| Published in |
Methods in molecular biology, January 2023
|
| DOI | 10.1007/978-1-0716-2899-7_18 |
| Pubmed ID | |
| Book ISBNs |
978-1-07-162898-0, 978-1-07-162899-7
|
| Authors |
Kim, Daniel S., Kim, Daniel S |
| Abstract |
DNA accessibility has been a powerful tool in locating active regulatory elements in a cell type, but dissecting the combinatorial logic within these regulatory elements has been a continued challenge in the field. Deep learning models have been shown to be highly predictive models of regulatory DNA and have led to new biological insights on regulatory syntax and logic. Here, we provide a framework for deep learning in genomics that implements best practices and focuses on ease of use, versatility, and compatibility with existing tools for inference on DNA sequence. |
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 % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 1 | 33% |
| Student > Bachelor | 1 | 33% |
| Unknown | 1 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 1 | 33% |
| Engineering | 1 | 33% |
| Unknown | 1 | 33% |
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 23 February 2023.
All research outputs
#18,733,166
of 23,885,338 outputs
Outputs from Methods in molecular biology
#7,638
of 13,512 outputs
Outputs of similar age
#297,124
of 436,044 outputs
Outputs of similar age from Methods in molecular biology
#372
of 605 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,512 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 38th percentile – i.e., 38% 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 436,044 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 605 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.