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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Accurate Ensemble Prediction of Somatic Mutations with SMuRF2.
|
|---|---|
| Chapter number | 4 |
| Book title |
Variant Calling
|
| Published in |
Methods in molecular biology, January 2022
|
| DOI | 10.1007/978-1-0716-2293-3_4 |
| Pubmed ID | |
| Book ISBNs |
978-1-07-162292-6, 978-1-07-162293-3
|
| Authors |
Huang, Weitai, Sim, Ngak Leng, Skanderup, Anders J, Skanderup, Anders J. |
| Abstract |
Accurate identification of somatic mutations is crucial for discovery and identification of driver mutations in cancer tumors. Here, we describe the updated Somatic Mutation calling method using a Random Forest (SMuRF2), an ensemble method that combines the predictions and auxiliary features from individual mutation callers using supervised machine learning. SMuRF2 provides an efficient workflow to predict both somatic point mutations (SNVs) and small insertions/deletions (indels) in cancer genomes and exomes. We describe the latest method and provide a detailed tutorial for running SMuRF2. |
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 % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
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 % |
|---|---|---|
| Researcher | 2 | 67% |
| Student > Bachelor | 1 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 67% |
| Engineering | 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 24 August 2022.
All research outputs
#19,103,731
of 24,323,943 outputs
Outputs from Methods in molecular biology
#7,851
of 13,696 outputs
Outputs of similar age
#358,473
of 509,051 outputs
Outputs of similar age from Methods in molecular biology
#422
of 814 outputs
Altmetric has tracked 24,323,943 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 13,696 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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We're also able to compare this research output to 814 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.