title: Morphology cluster and prediction of growth of human brain pyramidal neurons.
abstract: Predicting neuron growth is valuable to understand the morphology of neurons, thus it is helpful in the research of neuron classification. This study sought to propose a new method of predicting the growth of human neurons using 1 907 sets of data in human brain pyramidal neurons obtained from the website of NeuroMorpho.Org. First, we analyzed neurons in a morphology field and used an expectation-maximization algorithm to specify the neurons into six clusters. Second, naive Bayes classifier was used to verify the accuracy of the expectation-maximization algorithm. Experiment results proved that the cluster groups here were efficient and feasible. Finally, a new method to rank the six expectation-maximization algorithm clustered classes was used in predicting the growth of human pyramidal neurons.
authors: School of Computer, China University of Geosciences, Wuhan 430074, Hubei Province, China.
authors: Chao
authors: C
authors: Yu
authors: School of Computer, China University of Geosciences, Wuhan 430074, Hubei Province, China.
authors: Zengxin
authors: Z
authors: Han
authors: School of Computer, China University of Geosciences, Wuhan 430074, Hubei Province, China.
authors: Wencong
authors: W
authors: Zeng
authors: School of Science, South China University of Technology, Guangzhou 510640, Guangdong Province, China.
authors: Shenquan
authors: S
authors: Liu
keywords: 10-fold cross validation; expectation-maximization; morphological cluster; naive Bayes; neural regeneration; neurons