A gene selection algorithm based on the gene regulation probability using maximal likelihood estimation.
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.
Intelligent Computation Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Science, P.O. Box 1130, 230031, Hefei, Anhui, China. hqwang@iim.ac.cn
Hong-Qiang
HQ
Wang
De-Shuang
DS
Huang
D000208:Acute Disease; D000465:Algorithms; D020869:Gene Expression Profiling / Q000145:classification* / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D006801:Humans; D007951:Leukemia, Myeloid / Q000145:classification / Q000235:genetics; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000145:classification / Q000235:genetics; D015203:Reproducibility of Results