ArticleBioinformatics (Oxford, England)2018
A new approach for interpreting Random Forest models and its application to the biology of ageing.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ageing transcriptome meta-analysis reveals similarities and differences between key mammalian tissues.Aging · 2021Pooled it
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- Pushing the boundaries of few-shot learning for low-data drug discovery with a Bayesian meta-learning hypernetwork framework.Briefings in bioinformatics · 2025Article
- Epigenetic ageing clocks: statistical methods and emerging computational challenges.Nature reviews. Genetics · 2025Review
- Inhibition of CYP450 family 1 subfamily B member 1 (CYP1B1) expression in macrophage reduces the inflammatory response in type 2 diabetes mellitus combined with tuberculosis.Frontiers in endocrinology · 2025Article
- A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2024Article
- Identification of key biomarkers for predicting CAD progression in inflammatory bowel disease via machine-learning and bioinformatics strategies.Journal of cellular and molecular medicine · 2024Article
- Identification of potential biomarkers from amino acid transporter in the activation of hepatic stellate cells via bioinformatics.Frontiers in genetics · 2024Article
- Cytometric analysis reveals an association between allergen-responsive natural killer cells and human peanut allergy.The Journal of clinical investigation · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Identification and Validation of Immune Markers in Coronary Heart Disease.Computational and mathematical methods in medicine · 2022Article
- Article
- Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States.Sustainable cities and society · 2021Article
- Using deep learning to associate human genes with age-related diseases.Bioinformatics (Oxford, England) · 2020Article
- Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction.Frontiers in medicine · 2020Article
- Artificial intelligence for precision oncology: beyond patient stratification.NPJ precision oncology · 2019Review
- A Survey of Data Mining and Deep Learning in Bioinformatics.Journal of medical systems · 2018Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.