Title |
Robustness of Random Forest-based gene selection methods
|
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Published in |
BMC Bioinformatics, January 2014
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DOI | 10.1186/1471-2105-15-8 |
Pubmed ID | |
Authors |
Miron Bartosz Kursa |
Abstract |
Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon. At the same time, gene selection is very difficult because of the noisy nature of microarray data. As a consequence, gene selection is often performed with machine learning methods. The Random Forest method is particularly well suited for this purpose. In this work, four state-of-the-art Random Forest-based feature selection methods were compared in a gene selection context. The analysis focused on the stability of selection because, although it is necessary for determining the significance of results, it is often ignored in similar studies. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 2 | 50% |
Norway | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 2 | <1% |
Denmark | 2 | <1% |
Netherlands | 1 | <1% |
Austria | 1 | <1% |
Turkey | 1 | <1% |
Iran, Islamic Republic of | 1 | <1% |
Germany | 1 | <1% |
China | 1 | <1% |
United States | 1 | <1% |
Other | 0 | 0% |
Unknown | 207 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 62 | 28% |
Researcher | 38 | 17% |
Student > Master | 30 | 14% |
Student > Bachelor | 12 | 6% |
Professor | 6 | 3% |
Other | 27 | 12% |
Unknown | 43 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 45 | 21% |
Agricultural and Biological Sciences | 36 | 17% |
Biochemistry, Genetics and Molecular Biology | 21 | 10% |
Medicine and Dentistry | 16 | 7% |
Engineering | 14 | 6% |
Other | 40 | 18% |
Unknown | 46 | 21% |