PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection

Jing Wang; Dabing Zhang; Jing Li

doi:10.1186/1752-0509-7-s5-s9

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PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection

Jing Wang Dabing Zhang Jing Li

Year: 2013 Journal: BMC Systems Biology Vol: 7 (S5)Pages: S9-S9 Publisher: Springer Science+Business Media

DOI: 10.1186/1752-0509-7-s5-s9

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Keywords:

Feature selection Computer science Computational biology Redundancy (engineering) Support vector machine Subcellular localization Selection (genetic algorithm) Machine learning Artificial intelligence Biology Biochemistry Gene

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Probiotics and Fermented Foods

Life Sciences → Agricultural and Biological Sciences → Food Science

Identification and Quantification in Food

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Protein Hydrolysis and Bioactive Peptides

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection

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