Hybrid feature selection method for biomedical datasets

Solorio-Fernández, Saúl; Martínez-Trinidad, José Fco.; Carrasco-Ochoa, Jesús Ariel; Zhang, Yanqing

doi:10.1109/cibcb.2012.6217224

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…Due to the emergence of new technologies such as the micro array data, these new technologies produce large datasets characterized by a large number of features (genes); this is why feature selection (gene selection) has become very important in several fields such as Bioinformatics. Authors in [6,11] introduced a new hybrid feature selection method that combines the advantages of filter strategy based on the Laplacian Score joint with a simple wrapper strategy. The suggested algorithm resulted in a fast hybrid feature selectors that can solve feature selection problems in high dimensional datasets and select a small subset full of informative genes that is most relative to cancer classification.…”

Section: Introductionmentioning

confidence: 99%

Algorithms Implemented for Cancer Gene Searching and Classifications

Al-Rajab

2014

Bioinformatics Research and Applications

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Abstract. Understanding the gene expression is an important factor to cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no an obvious exact algorithm that can be implemented individually for various cancer cells. In this paper a research is conducted through the most common top ranked algorithms implemented for cancer gene search and classification, and how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for Bio image analysis for cancer cells and algorithms implemented based on DNA array data. The main purpose of this paper is to explore a road map towards presenting the most current algorithms implemented for cancer gene search and classification.

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Section: Introductionmentioning

confidence: 99%