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X Demographics
Mendeley readers
Attention Score in Context
Chapter title |
Machine Learning Approaches to Analyze MALDI-TOF Mass Spectrometry Protein Profiles.
|
---|---|
Chapter number | 29 |
Book title |
Multiplex Biomarker Techniques
|
Published in |
Methods in molecular biology, January 2022
|
DOI | 10.1007/978-1-0716-2395-4_29 |
Pubmed ID | |
Book ISBNs |
978-1-07-162394-7, 978-1-07-162395-4
|
Authors |
Lazari, Lucas C., Rosa-Fernandes, Livia, Palmisano, Giuseppe |
Abstract |
Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification. |
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 % |
---|---|---|
Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 2 | 22% |
Professor > Associate Professor | 2 | 22% |
Unspecified | 1 | 11% |
Student > Ph. D. Student | 1 | 11% |
Unknown | 3 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 2 | 22% |
Biochemistry, Genetics and Molecular Biology | 1 | 11% |
Unspecified | 1 | 11% |
Chemistry | 1 | 11% |
Immunology and Microbiology | 1 | 11% |
Other | 0 | 0% |
Unknown | 3 | 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 16 July 2022.
All research outputs
#20,323,943
of 22,867,327 outputs
Outputs from Methods in molecular biology
#9,916
of 13,128 outputs
Outputs of similar age
#409,840
of 500,167 outputs
Outputs of similar age from Methods in molecular biology
#401
of 593 outputs
Altmetric has tracked 22,867,327 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 13,128 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 593 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.