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Ten quick tips for machine learning in computational biology

Overview of attention for article published in BioData Mining, December 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#1 of 300)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

twitter
375 tweeters
facebook
5 Facebook pages
wikipedia
15 Wikipedia pages
googleplus
2 Google+ users
reddit
1 Redditor
q&a
1 Q&A thread

Citations

dimensions_citation
401 Dimensions

Readers on

mendeley
987 Mendeley
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Title
Ten quick tips for machine learning in computational biology
Published in
BioData Mining, December 2017
DOI 10.1186/s13040-017-0155-3
Pubmed ID
Authors

Davide Chicco

Abstract

Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.

Twitter Demographics

The data shown below were collected from the profiles of 375 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 987 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 987 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 200 20%
Student > Master 162 16%
Researcher 156 16%
Student > Bachelor 124 13%
Student > Doctoral Student 53 5%
Other 120 12%
Unknown 172 17%
Readers by discipline Count As %
Computer Science 160 16%
Biochemistry, Genetics and Molecular Biology 159 16%
Agricultural and Biological Sciences 125 13%
Engineering 95 10%
Medicine and Dentistry 47 5%
Other 177 18%
Unknown 224 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 228. 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 10 June 2022.
All research outputs
#121,207
of 21,433,132 outputs
Outputs from BioData Mining
#1
of 300 outputs
Outputs of similar age
#3,663
of 444,088 outputs
Outputs of similar age from BioData Mining
#1
of 34 outputs
Altmetric has tracked 21,433,132 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 300 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one has done particularly well, scoring higher than 99% of its peers.
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 444,088 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.