A gene selection algorithm based on the gene regulation probability using maximal likelihood estimation.

Hong-Qiang Wang; De-Shuang Huang

doi:10.1007/s10529-005-3253-0

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A gene selection algorithm based on the gene regulation probability using maximal likelihood estimation.

Hong-Qiang Wang·De-Shuang Huang

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Abstract

A novel gene selection algorithm based on the gene regulation probability is proposed. In this algorithm, a probabilistic model is established to estimate gene regulation probabilities using the maximum likelihood estimation method and then these probabilities are used to select key genes related by class distinction. The application on the leukemia data-set suggests that the defined gene regulation probability can identify the key genes to the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) class distinction and the result of our proposed algorithm is competitive to those of the previous algorithms.

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Acute Disease Algorithms Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Leukemia, Myeloid Precursor Cell Lymphoblastic Leukemia-Lymphoma Reproducibility of Results Comparative Study Journal Article

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Hong-Qiang Wang, De-Shuang Huang. 2005. A gene selection algorithm based on the gene regulation probability using maximal likelihood estimation.. https://doi.org/10.1007/s10529-005-3253-0

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