Discretization of gene expression data revised
Gene expression measurements represent the most important source of biological data used to unveil theinteraction and functionality of genes. In this regard, several data mining and machine learning algorithms havebeen proposed that require, in a number of cases, some kind of data discretization in...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/21610 |
| Acceso en línea: | http://hdl.handle.net/11336/21610 |
| Access Level: | acceso abierto |
| Palabra clave: | Data Preprocessing Data Mining Gene Expression Data Discretization Gene Expressiion Analysis Machine Learning https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | Gene expression measurements represent the most important source of biological data used to unveil theinteraction and functionality of genes. In this regard, several data mining and machine learning algorithms havebeen proposed that require, in a number of cases, some kind of data discretization in order to perform theinference. Selection of an appropriate discretization process has a major impact on the design and outcome of theinference algorithms, since there are a number of relevant issues that need to be considered. This study presents arevision of the current state of the art discretization techniques, together with the key subjects that need to beconsidered when designing or selecting a discretization approach for gene expression data. |
|---|