Putative synaptic genes defined from a Drosophila whole body developmental transcriptome by a machine learning approach
Number of Authors: 5
2015 (English)In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 16, 694Article in journal (Refereed) Published
Background: 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. Results: 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. Conclusions: The utility of our approach is that it reduces the number of genes to be tested through hypothesis-driven experimentation.
Place, publisher, year, edition, pages
2015. Vol. 16, 694
Synapse, Machine learning, Temporal transcription profiles
IdentifiersURN: urn:nbn:se:su:diva-121880DOI: 10.1186/s12864-015-1888-3ISI: 000361093400006OAI: oai:DiVA.org:su-121880DiVA: diva2:862632