[Probabilidad-Estadistica-Seminario] Seminario de Probabilidad y Estadística

[seminarios en cmat.edu.uy]

Seminario de Probabilidad y Estadística
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Título: "An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach"

Expositor: Flavio Pazos (IIBCE)

Resumen:
 
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.

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.
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Viernes 23/8 a las 10:30, Salón de seminarios del piso 14, CMAT

Contacto: Andrés Sosa - asosa en cmat.edu.uy
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Viernes 23/8 a las 10:30, Salón de   seminario  s del piso 14, CMAT    Seminario
de Probabilidad y Estadística    Expositor:  Flavio Pazos

ABSTRACT

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.
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