# Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/knowledge-slanted-random-forest-method-for-high-dimensional-data-and-small/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Érika Cantor,Sandra Guauque-Olarte,Roberto León,Stéren Chabert,Rodrigo Salas |
| Citations | 18 |
| DOI | 10.1186/s13040-024-00388-8 |
| Fields | Biochemistry, Genetics and Molecular Biology,Computer Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://link.springer.com/content/pdf/10.1186/s13040-024-00388-8.pdf |
| OpenAlex ID | https://openalex.org/W4402411761 |
| PMID | 39256872 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Érika Cantor](https://scholariq.org/researchers/erika-cantor/)

## Paper primary topic

- [Gene expression and cancer classification](https://scholariq.org/topics/gene-expression-and-cancer-classification/)

## Paper topics

- [Gene expression and cancer classification](https://scholariq.org/topics/gene-expression-and-cancer-classification/)
- [Evolutionary Algorithms and Applications](https://scholariq.org/topics/evolutionary-algorithms-and-applications/)
- [Neural Networks and Applications](https://scholariq.org/topics/neural-networks-and-applications/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.