ArticleBioData mining2024
Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data.
Article in BioData mining, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identification of candidate genes and metabolites associated with lactation performance in Kazakh mares using blood multi-omics and machine learning.BMC genomics · 2026Article
- Integrative machine learning analysis suggests novel molecular targets for liver cancer diagnosis and therapy.Discover oncology · 2026Article
- Machine Learning for Individual EV Classification Based on Highly Sensitive Multiplexed Mass Spectrometry Measurements.Journal of the American Society for Mass Spectrometry · 2026Article
- Precision diagnosis model for treatment-resistant depression integrating serum metabolomics and clinical risk factors.Frontiers in psychiatry · 2026Article
- Multi-omics mapping of PSC genetic risk to a high TWAS-active JAML+ lipid-associated macrophage program: bridging single-cell heterogeneity, spatial fibrotic niches, and machine learning selection.Frontiers in cell and developmental biology · 2026Article
- Identification of Interleukin-1β in Whole Blood as a Candidate Biomarker for Alcohol Use Disorder Risk Based on AUDIT Scores.Addiction biology · 2025Article
- Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics.Biomolecules · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The use of prior knowledge in the machine learning framework has been considered a potential tool to handle the curse of dimensionality in genetic and genomics data. Although random forest (RF) represents a flexible non-parametric approach with several advantages, it can provide poor accuracy in high-dimensional settings, mainly in scenarios with small sample sizes. We propose a knowledge-slanted RF that integrates biological networks as prior knowledge into the model to improve its performance and explainability, exemplifying its use for selecting and identifying relevant genes. knowledge-slanted RF is a combination of two stages. First, prior knowledge represented by graphs is translated by running a random walk with restart algorithm to determine the relevance of each gene based on its connection and localization on a protein-protein interaction network. Then, each relevance is used to modify the selection probability to draw a gene as a candidate split-feature in the conventional RF. Experiments in simulated datasets with very small sample sizes
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