Name
Title | ||
|---|---|---|
A comparative study of improvements Pre-filter methods bring on feature selection using microarray data. |
Abstract | ||
|---|---|---|
Feature selection techniques have become an apparent need in biomarker discoveries with the development of microarray. However, the high dimensional nature of microarray made feature selection become time-consuming. To overcome such difficulties, filter data according to the background knowledge before applying feature selection techniques has become a hot topic in microarray analysis. Different methods may affect final results greatly, thus it is important to evaluate these pre-filter methods in a system way.In this paper, we compared the performance of statistical-based, biological-based pre-filter methods and the combination of them on microRNA-mRNA parallel expression profiles using L1 logistic regression as feature selection techniques. Four types of data were built for both microRNA and mRNA expression profiles.Results showed that pre-filter methods could reduce the number of features greatly for both mRNA and microRNA expression datasets. The features selected after pre-filter procedures were shown to be significant in biological levels such as biology process and microRNA functions. Analyses of classification performance based on precision showed the pre-filter methods were necessary when the number of raw features was much bigger than that of samples. All the computing time was greatly shortened after pre-filter procedures.With similar or better classification improvements, less but biological significant features, pre-filter-based feature selection should be taken into consideration if researchers need fast results when facing complex computing problems in bioinformatics. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1186/2047-2501-2-7 | Health information science and systems |
Keywords | Field | DocType |
comparative study,feature selection,microarray | Data mining,Feature selection,Computer science,Microarray analysis techniques,Health informatics | Journal |
Volume | Issue | ISSN |
2 | 1 | 2047-2501 |
Citations | PageRank | References |
0 | 0.34 | 28 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingying Wang | 1 | 21 | 11.64 |
| Xiaomao Fan | 2 | 0 | 0.34 |
| Yunpeng Cai | 3 | 0 | 0.34 |