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10.1093/bioinformatics/bty087

http://scihub22266oqcxt.onion/10.1093/bioinformatics/bty087
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C6041990!6041990!29462247
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suck abstract from ncbi


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pmid29462247      Bioinformatics 2018 ; 34 (14): 2449-56
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  • A new approach for interpreting Random Forest models and its application to the biology of ageing #MMPMID29462247
  • Fabris F; Doherty A; Palmer D; de Magalhćes JP; Freitas AA
  • Bioinformatics 2018[Jul]; 34 (14): 2449-56 PMID29462247show ga
  • Motivation: This work uses the Random Forest (RF) classification algorithm to predict if a gene is over-expressed, under-expressed or has no change in expression with age in the brain. RFs have high predictive power, and RF models can be interpreted using a feature (variable) importance measure. However, current feature importance measures evaluate a feature as a whole (all feature values). We show that, for a popular type of biological data (Gene Ontology-based), usually only one value of a feature is particularly important for classification and the interpretation of the RF model. Hence, we propose a new algorithm for identifying the most important and most informative feature values in an RF model. Results: The new feature importance measure identified highly relevant Gene Ontology terms for the aforementioned gene classification task, producing a feature ranking that is much more informative to biologists than an alternative, state-of-the-art feature importance measure. Availability and implementation: The dataset and source codes used in this paper are available as ?Supplementary Material? and the description of the data can be found at: https://fabiofabris.github.io/bioinfo2018/web/. Supplementary information: Supplementary data are available at Bioinformatics online.
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