combinatorial expression rules of ion channel genes in juvenile rat (rattus norvegicus) neocortical neurons离子通道基因的组合表达式规则在幼年大鼠皮层神经元(鼠形).pdfVIP

combinatorial expression rules of ion channel genes in juvenile rat (rattus norvegicus) neocortical neurons离子通道基因的组合表达式规则在幼年大鼠皮层神经元(鼠形).pdf

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combinatorial expression rules of ion channel genes in juvenile rat (rattus norvegicus) neocortical neurons离子通道基因的组合表达式规则在幼年大鼠皮层神经元(鼠形)

Combinatorial Expression Rules of Ion Channel Genes in Juvenile Rat (Rattus norvegicus) Neocortical Neurons ¨ Georges Khazen, Sean L. Hill, Felix Schurmann, Henry Markram* ´ ´ Blue Brain Project, Brain Mind Institute, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland Abstract The electrical diversity of neurons arises from the expression of different combinations of ion channels. The gene expression rules governing these combinations are not known. We examined the expression of twenty-six ion channel genes in a broad range of single neocortical neuron cell types. Using expression data from a subset of twenty-six ion channel genes in ten different neocortical neuronal types, classified according to their electrophysiological properties, morphologies and anatomical positions, we first developed an incremental Support Vector Machine (iSVM) model that prioritizes the predictive value of single and combinations of genes for the rest of the expression pattern. With this approach we could predict the expression patterns for the ten neuronal types with an average 10-fold cross validation accuracy of 87% and for a further fourteen neuronal types not used in building the model, with an average accuracy of 75%. The expression of the genes for HCN4, Kv2.2, Kv3.2 and Cab3 were found to be particularly strong predictors of ion channel gene combinations, while expression of the Kv1.4 and Kv3.3 genes has no predictive value. Using a logic gate analysis, we then extracted a spectrum of observed combinatorial gene expression rules of twenty ion channels in different neocortical neurons. We also show that when applied to a completely random and independent data, the model could not extract any rules

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