scholarly journals Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model

2020 ◽  
Vol 30 (12) ◽  
pp. 1835-1845
Author(s):  
Li Tang ◽  
Matthew C. Hill ◽  
Jun Wang ◽  
Jianxin Wang ◽  
James F. Martin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Ensemble Machine Learning ◽  
Machine Learning Model

scholarly journals Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145968-145983 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Ataollah Shirzadi ◽  
Bahram Choubin ◽  
Fereshteh Taromideh ◽  
Farzaneh Sajedi Hosseini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Susceptibility Mapping ◽  
Snow Avalanche ◽  
Random Subspace ◽  
Ensemble Machine Learning ◽  
Machine Learning Model ◽  
Tree Classifier

scholarly journals An improved catalogue of putative synaptic genes defined by their temporal transcription profiles through an ensemble machine learning approach

2019 ◽  
Author(s):  
Flavio Pazos ◽  
Pablo Soto ◽  
Martín Palazzo ◽  
Gustavo Guerberoff ◽  
Patricio Yankilevich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Empirical Data ◽  
Predictive Power ◽  
Learning Model ◽  
Synaptic Function ◽  
Training Set ◽  
Training Scheme ◽  
Transcription Profiles ◽  
Ensemble Machine Learning ◽  
Machine Learning Model

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 that was postulated to be very enriched in genes with still undocumented synaptic functions. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we used these new empirical data to evaluate the predictive power of the catalogue. Then we implemented a series of improvements to the training scheme and the ensemble rules of our model and added the new synaptic genes to the training set, to obtain a new, enhanced catalogue of putative synaptic genes. Results. The retrospective analysis demonstrated that our original catalogue was indeed highly enriched in genes with unknown synaptic function. The changes to the training scheme and the ensemble rules resulted in a catalogue with better predictive power. Finally, training this improved model with an updated training set, that includes all the new synaptic genes, we obtained a new, enhanced catalogue of putative synaptic genes, which we present here announcing a regularly updated version that will be available online at: http://synapticgenes.bnd.edu.uy Conclusions. We show that training a machine learning model 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, we validated our original approach, improved our model an obtained a better catalogue. The utility of this approach is that it reduces the number of genes to be tested through hypothesis-driven experimentation.

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