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Research article
Flavio Pazos Obregón
Instituto de Investigaciones Biologicas Clemente Estable
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4980-1071
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Genomics.
Background . Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes which we postulated to be very enriched in genes with a still undocumented synaptic function. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we use these new empirical data to evaluate our original prediction. We also implement a series of changes to the training scheme of our model and using the new data we demonstrate that this improves its predictive power. Finally, we added the new synaptic genes to the training set and trained a new model, obtaining a new, enhanced catalogue of putative synaptic genes.
Results . The retrospective analysis demonstrate that our original catalogue was significantly enriched in new synaptic genes. When the changes to the training scheme were implemented using the original training set we obtained even higher enrichment. Finally, applying the new training scheme with a training set including the 79 new synaptic genes, resulted in an enhanced catalogue of putative synaptic genes. Here we present this new catalogue and announce that a regularly updated version will be available online at: http://synapticgenes.bnd.edu.uy
Conclusions . We show that training an ensemble of machine learning classifiers solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data provided by the scientific community, we validated our original approach, improved our model an obtained an arguably more precise prediction. This approach reduces the number of genes to be tested through hypothesis-driven experimentation and will facilitate our understanding of neuronal function.
Availability : http://synapticgenes.bnd.edu.uy
Keywords
Synaptic genes, Machine learning, Temporal transcription profiles, Gene function prediction, Drosophila melanogaster.
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CURRENT STATUS: Published
Version 3
Posted 30 Oct, 2019
Published
On 23 Dec, 2019
No community comments so far
Editorial decision: Accept
On 09 Dec, 2019
Review #2 received
Received 01 Dec, 2019
Review #1 received
Received 26 Nov, 2019
Reviewer #2 agreed
On 20 Nov, 2019
Reviewer #1 agreed
On 19 Nov, 2019
Editor assigned
On 18 Nov, 2019
Reviewers invited
Invitations sent on 18 Nov, 2019
Submission checks complete
On 17 Nov, 2019
Editor invited
On 17 Nov, 2019
No comments provided
Editorial decision: Major revision
On 04 Oct, 2019
Review #2 received
Received 30 Sep, 2019
Review #1 received
Received 27 Sep, 2019
Reviewer #2 agreed
On 12 Sep, 2019
Reviewers invited
Invitations sent on 06 Sep, 2019
Reviewer #1 agreed
On 06 Sep, 2019
Editor assigned
On 03 Sep, 2019
Submission checks complete
On 02 Sep, 2019
Editor invited
On 02 Sep, 2019
No comments provided
Editorial decision: Revise before sending to peer reviewers
On 30 Aug, 2019
Submission checks complete
On 27 Aug, 2019
Editor invited
On 27 Aug, 2019
Editor assigned
On 20 Aug, 2019
First submitted
On 16 Aug, 2019
Metrics
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HTML Views: ...
Subject Areas
Epigenetics & Genomics
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This preprint is under consideration at BMC Genomics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Flavio Pazos Obregón
Instituto de Investigaciones Biologicas Clemente Estable
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4980-1071
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 3
Posted 30 Oct, 2019
Published
On 23 Dec, 2019
No community comments so far
Editorial decision: Accept
On 09 Dec, 2019
Review #2 received
Received 01 Dec, 2019
Review #1 received
Received 26 Nov, 2019
Reviewer #2 agreed
On 20 Nov, 2019
Reviewer #1 agreed
On 19 Nov, 2019
Editor assigned
On 18 Nov, 2019
Reviewers invited
Invitations sent on 18 Nov, 2019
Submission checks complete
On 17 Nov, 2019
Editor invited
On 17 Nov, 2019
No comments provided
Editorial decision: Major revision
On 04 Oct, 2019
Review #2 received
Received 30 Sep, 2019
Review #1 received
Received 27 Sep, 2019
Reviewer #2 agreed
On 12 Sep, 2019
Reviewers invited
Invitations sent on 06 Sep, 2019
Reviewer #1 agreed
On 06 Sep, 2019
Editor assigned
On 03 Sep, 2019
Submission checks complete
On 02 Sep, 2019
Editor invited
On 02 Sep, 2019
No comments provided
Editorial decision: Revise before sending to peer reviewers
On 30 Aug, 2019
Submission checks complete
On 27 Aug, 2019
Editor invited
On 27 Aug, 2019
Editor assigned
On 20 Aug, 2019
First submitted
On 16 Aug, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Genomics.
Background . Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes which we postulated to be very enriched in genes with a still undocumented synaptic function. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we use these new empirical data to evaluate our original prediction. We also implement a series of changes to the training scheme of our model and using the new data we demonstrate that this improves its predictive power. Finally, we added the new synaptic genes to the training set and trained a new model, obtaining a new, enhanced catalogue of putative synaptic genes.
Results . The retrospective analysis demonstrate that our original catalogue was significantly enriched in new synaptic genes. When the changes to the training scheme were implemented using the original training set we obtained even higher enrichment. Finally, applying the new training scheme with a training set including the 79 new synaptic genes, resulted in an enhanced catalogue of putative synaptic genes. Here we present this new catalogue and announce that a regularly updated version will be available online at: http://synapticgenes.bnd.edu.uy
Conclusions . We show that training an ensemble of machine learning classifiers solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data provided by the scientific community, we validated our original approach, improved our model an obtained an arguably more precise prediction. This approach reduces the number of genes to be tested through hypothesis-driven experimentation and will facilitate our understanding of neuronal function.
Availability : http://synapticgenes.bnd.edu.uy
Figure 1
Figure 2
Figure 3
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