Putative synaptic genes defined from a Drosophila whole body developmental transcriptome by a machine learning approach
Copyright policy )Putative synaptic genes defined from a Drosophila whole body developmental transcriptome by a machine learning approach
Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Although roughly a thousand genes are expected to be important for this function in Drosophila melanogaster, just a few hundreds of them are known so far.In this work we trained three learning algorithms to predict a "synaptic function" for genes of Drosophila using data from a whole-body developmental transcriptome published by others. Using statistical and biological criteria to analyze and combine the predictions, we obtained a gene catalogue that is highly enriched in genes of relevance for Drosophila synapse assembly and function but still not recognized as such.The utility of our approach is that it reduces the number of genes to be tested through hypothesis-driven experimentation.
Gene Expression Profiling, Computational Biology, Datasets as Topic, Gene Expression Regulation, Developmental, Models, Biological, Rats, Machine Learning, Organ Specificity, Synapses, Genetics, Animals, Humans, Drosophila, Transcriptome, Algorithms, Biotechnology, Research Article
Gene Expression Profiling, Computational Biology, Datasets as Topic, Gene Expression Regulation, Developmental, Models, Biological, Rats, Machine Learning, Organ Specificity, Synapses, Genetics, Animals, Humans, Drosophila, Transcriptome, Algorithms, Biotechnology, Research Article
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